online learning during pandemic essay

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• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

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Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

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1 Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

2 Literature review

2.1 education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

2.2 Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

2.3 Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

3 Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

3.1 Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

3.2 Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

3.3 Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

4.1 The extent of students’ online learning challenges

Table 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x̅  = 2.62, SD  = 1.03) with scores ranging from x̅  = 1.72 ( to some extent ) to x̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x̅  = 2.51, SD  = 1.31), SIC ( x̅  = 2.77, SD  = 1.34), and LRC ( x̅  = 2.93, SD  = 1.31).

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

4.2 Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

4.3 Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary.

5 Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Barrot, J.S., Llenares, I.I. & del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ Inf Technol 26 , 7321–7338 (2021). https://doi.org/10.1007/s10639-021-10589-x

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ORIGINAL RESEARCH article

Engagement in online learning: student attitudes and behavior during covid-19.

• 1 Department of Mathematics, University of California, San Diego, San Diego, CA, United States
• 2 Halıcıo ǧ lu Data Science Institute, University of California, San Diego, San Diego, CA, United States
• 3 Joint Doctoral Program in Math and Science Education, San Diego State University, San Diego, CA, United States
• 4 Joint Doctoral Program in Math and Science Education, University of California, San Diego, San Diego, CA, United States
• 5 Department of Physical Therapy, Movement and Rehabilitation Science, Bouvé College of Health, Northeastern University, Boston, MA, United States
• 6 Art and Design, College of Arts, Media and Design, Northeastern University, Boston, MA, United States
• 7 Qualcomm Institute, University of California, San Diego, San Diego, CA, United States

The COVID-19 pandemic resulted in nearly all universities switching courses to online formats. We surveyed the online learning experience of undergraduate students ( n = 187) at a large, public research institution in course structure, interpersonal interaction, and academic resources. Data was also collected from course evaluations. Students reported decreases in live lecture engagement and attendance, with 72 percent reporting that low engagement during lectures hurt their online learning experience. A majority of students reported that they struggled with staying connected to their peers and instructors and managing the pace of coursework. Students had positive impressions, however, of their instructional staff. Majorities of students felt more comfortable asking and answering questions in online classes, suggesting that there might be features of learning online to which students are receptive, and which may also benefit in-person classes.

Introduction

In Spring 2020, 90% of higher education institutions in the United States canceled in-person instruction and shifted to emergency remote teaching (ERT) due to the COVID-19 pandemic ( Lederman, 2020 ). ERT in response to COVID-19 is qualitatively different from typical online learning instruction as students did not self-select to participate in ERT and teachers were expected to transition to online learning in an unrealistic time frame ( Brooks et al., 2020 ; Hodges et al., 2020 ; Johnson et al., 2020 ). This abrupt transition left both faculty and students without proper preparation for continuing higher education in an online environment.

In a random sample of 1,008 undergraduates who began their Spring 2020 courses in-person and ended them online, 51% of respondents said they were very satisfied with their course before the pandemic, and only 19% were very satisfied after the transition to online learning ( Means and Neisler, 2020 ). Additionally, 57% of respondents said that maintaining interest in the course material was “worse online,” 65% claimed they had fewer opportunities to collaborate with peers, and 42% said that keeping motivated was a problem ( Means and Neisler, 2020 ). Another survey of 3,089 North American higher education students had similar results with 78% of respondents saying online experiences were not engaging and 75% saying they missed face-to-face interactions with instructors and peers ( Read, 2020 ). Lastly, of the 97 university presidents surveyed in the United States by Inside Higher Ed , 81% claimed that maintaining student engagement would be challenging when moving classes online due to COVID-19 ( Inside Higher Ed, 2020 ).

In this report, we consider the measures and strategies that were implemented to engage students in online lectures at UCSD during ERT due to the COVID-19 pandemic. We investigate student perceptions of these measures and place our findings in the larger context of returning to in-person instruction and improving engagement in both online and in-person learning for undergraduates. Before diving into the current study, we first define what we mean by engagement.

Theoretical Framework and Literature Review

Student engagement.

Student engagement has three widely accepted dimensions: behavioral, cognitive and affective ( Chapman, 2002 ; Fredricks et al., 2004 , 2016 ; Mandernach, 2015 ). Each dimension has indicators ( Fredricks et al., 2004 ), or facets ( Coates, 2007 ), that manifest each dimension. Behavioral engagement refers to active responses to learning activities and is indicated by participation, persistence, and/or positive conduct. Cognitive engagement includes mental effort in learning activities and is indicated by deep learning, self-regulation, and understanding. Affective engagement is the emotional investment in learning activities and is indicated by positive reactions to the learning environment, peers, and teachers as well as a sense of belonging. A list of indicators for each dimension can be found in Bond et al. (2020) .

The literature also theorizes different influences for each engagement dimension. Most influencing factors are sociocultural in nature and can include the political, social, and teaching environment as well as relationships within the classroom ( Kahu, 2013 ). In particular, social engagement with peers and instructors creates a sense of community, which is often correlated with more effective learning outcomes ( Rovai and Wighting, 2005 ; Liu et al., 2007 ; Lear et al., 2010 ; Kendricks, 2011 ; Redmond et al., 2018 ; Chatterjee and Correia, 2020 ). Three key classroom interactions are often investigated when trying to understand the factors influencing student engagement: student-student interactions, student-instructor interactions, and student-content interactions ( Moore, 1993 ).

Student-student interactions prevent boredom and isolation by creating a dynamic sense of community ( Martin and Bolliger, 2018 ). Features that foster student-student interactions in online learning environments include group activities, peer assessment, and use of virtual communication spaces such as social media, chat forums, and discussion boards ( Revere and Kovach, 2011 ; Tess, 2013 ; Banna et al., 2015 ). In the absence of face-to-face communication, these virtual communication spaces help build student relationships ( Nicholson, 2002 ; Harrell, 2008 ). In a survey of 1,406 university students in asynchronous online courses, the students claimed to have greater satisfaction and to have learned more when more of the course grade was based on discussions, likely because discussions fostered increased student-student and student-instructor interactions ( Shea et al., 2001 ). Interestingly, in another study, graduate students in online courses claimed that student-student interactions were the least important of the three for maintaining student engagement, but that they were more likely to be engaged if an online course had online communication tools, ice breakers, and group activities ( Martin and Bolliger, 2018 ).

In the Martin and Bolliger (2018) study, the graduate students enrolled in online courses found student-instructor interactions to be the most important of the three interaction types, which supports prior work that found students perceive student-instructor interactions as more important than peer interactions in fostering engagement ( Swan and Shih, 2005 ). Student-instructor interactions increased in frequency in online classes when the following practices were implemented (1) multiple open communication channels between students and instructors ( Gaytan and McEwen, 2007 ; Dixson, 2010 ; Martin and Bolliger, 2018 ), (2) regular communication of announcements, reminders, grading rubrics, and expectations by instructors ( Martin and Bolliger, 2018 ), (3) timely and consistent feedback provided to students ( Gaytan and McEwen, 2007 ; Dixson, 2010 ; Chakraborty and Nafukho, 2014 ; Martin and Bolliger, 2018 ), and (4) instructors taking a minimal role in course discussions ( Mandernach et al., 2006 ; Dixson, 2010 ).

Student-content interactions include any interaction the student has with course content. Qualities that have been shown to increase student engagement with course content include the use of curricular materials and classroom activities that incorporate realistic scenarios, prompts that scaffold deep reflection and understanding, multimedia instructional materials, and those that allow student agency in choice of content or activity format ( Abrami et al., 2012 ; Wimpenny and Savin-Baden, 2013 ; Britt et al., 2015 ; Martin and Bolliger, 2018 ). In online learning, students need to be able to use various technologies in order to be able to engage in student-content interactions, so technical barriers such as lack of access to devices or reliable internet can be a substantial issue that deprives educational opportunities especially for students from lower socioeconomic households ( Means and Neisler, 2020 ; Reich et al., 2020 ; UNESCO, 2020 ).

Engagement in Online Learning

Bond and Bedenlier (2019) present a theoretical framework for engagement in online learning that combines the three dimensions of engagement, types of interactions that can influence the engagement dimensions, and possible short term and long term outcomes. The types of interactions are based on components present in the student’s immediate surrounding or microsystem, and are largely based on Moore’s three types of interactions: teachers, peers, and curriculum. However, the authors add technology and the classroom environment as influential components because they are particularly important for online learning.

Specific characteristics of each microsystem component can differentially modulate student engagement, and each component has at least one characteristic that specifically focuses on technology. Teacher presence, feedback, support, time invested, content expertise, information and communications technology skills and knowledge, technology acceptance, and use of technology all can influence the types of interactions students might have with their teachers which would then impact their engagement ( Zhu, 2006 ; Beer et al., 2010 ; Zepke and Leach, 2010 ; Ma et al., 2015 ; Quin, 2017 ). For curriculum/activities, the quality, design, difficulty, relevance, level of required collaboration, and use of technology can influence the types of interactions a student might encounter that could impact their engagement ( Zhu, 2006 ; Coates, 2007 ; Zepke and Leach, 2010 ; Bundick et al., 2014 ; Almarghani and Mijatovic, 2017 ; Xiao, 2017 ). Characteristics that can change the quantity and quality of peer interactions and thereby influence engagement include the amount of opportunities to collaborate, formation of respectful relationships, clear boundaries and expectations, being able to physically see each other, and sharing work with others and in turn respond to the work of others ( Nelson Laird and Kuh, 2005 ; Zhu, 2006 ; Yildiz, 2009 ; Zepke and Leach, 2010 ). When describing influential characteristics, the authors combine classroom environment and technology because in online learning, the classroom environment inherently utilizes technology. The influential characteristics of these two components are access to technology, support in using and understanding technology, usability, design, technology choice, sense of community, and types of assessment measures. All of these characteristics demonstrably influenced engagement levels in prior literature ( Zhu, 2006 ; Dixson, 2010 ; Cakir, 2013 ; Levin et al., 2013 ; Martin and Bolliger, 2018 ; Northey et al., 2018 ; Sumuer, 2018 ).

Online learning can take place in different formats, including fully synchronous, fully asynchronous, or blended ( Fadde and Vu, 2014 ). Each of these formats offers different challenges and opportunities for technological ease, time management, community, and pacing. Fully asynchronous learning is time efficient, but offers less opportunity for interactions that naturally take place in person ( Fadde and Vu, 2014 ). Instructors and students may feel underwhelmed by the lack of immediate feedback that can happen in face to face class time ( Fadde and Vu, 2014 ). Synchronous online learning is less flexible for teachers and students and requires reliable technology, but allows for more real time engagement and feedback ( Fadde and Vu, 2014 ). In blended learning courses, instructors have to coordinate and organize both the online and in person meetings and lessons, which is not as time efficient. Blended learning means there is some in person engagement which provides spontaneity and more natural personal relations ( Fadde and Vu, 2014 ). In all online formats, students may feel isolated and instructors and students need to spend more time and intention into building community ( Fadde and Vu, 2014 ; Gillett-Swan, 2017 ). Often, instructors can use learning management systems and discussion boards to help facilitate student interaction and connection ( Fadde and Vu, 2014 ). In terms of group work, engagement and participation is dependent not only on the modality of learning, but also the instructors expectations for assessment ( Gillett-Swan, 2017 ). Given the flexibility and power of online meeting and work environments, collaborating synchronously or asynchronously are both possible and effective ( Gillett-Swan, 2017 ). In online learning courses, especially fully asynchronous, students are more accountable for their learning, which may be challenging for students who struggle with self-regulating their work pace ( Gillett-Swan, 2017 ). Learning from home also means there are more distractions than when students attend class on campus. At any point during class, children, pets, or work can interrupt a student’s, or instructor’s, remote learning or teaching ( Fadde and Vu, 2014 ).

According to Raes et al. (2019) , the flexibility of a blended -or hybrid- learning environment encourages more students to show up to class when they otherwise would have taken a sick day, or would not have been able to attend due to home demands. It also equalizes learning opportunities for underrepresented groups, and more comprehensive support with two modes of interaction. On the other hand, hybrid learning can cause more strain on the instructor who may have to adapt their teaching designs for the demands of this unique format while maintaining the same standards ( Bülow, 2022 ). Due to the nature of class, some students can feel more distant to the instructor and to each other, and in many cases active class participation was difficult in hybrid learning environments. Although Bulow’s review (2022) focused on the challenges and opportunities of designing effective hybrid learning environments for the teacher, it follows that students participating in different environments will also need to adapt to foster effective active participation environments that encompass both local and remote learners.

Engagement in Emergency Remote Instruction During COVID-19

There is currently a thin literature on student perceptions of the efficacy of ERT strategies and formats in engaging students during COVID-19. Indeed, student perceptions about online learning do not indicate actual learning. This study considers student perceptions for the purpose of gathering information about what conditions help or hinder students’ comfort with engaging in online classes toward the goal of designing improved online learning opportunities in the future. The large scale surveys of undergraduate students had some items relating to engagement, but these surveys aimed to generally understand the student experience during the transition to COVID-19 induced ERT ( Means and Neisler, 2020 ; Read, 2020 ). A few small studies have surveyed or interviewed students from a single course on their perceptions of the changes made to courses to accommodate ERT ( Senn and Wessner, 2021 ), the positives and negatives of ERT ( Hussein et al., 2020 ), or the changes in their participation patterns and the course structures and instructor strategies that increase or decrease engagement in ERT ( Perets et al., 2020 ). In their survey of 73 students across the United States, Wester and colleagues specifically focused on changes to students’ cognitive, affective, and behavioral engagement due to COVID-19 induced ERT, but they did not inquire as to what were the key influencing factors for these changes. Walker and Koralesky (2021) and Shin and Hickey (2021) surveyed students from a single institution but from multiple courses and thus are most relevant to the current study. These studies aimed to understand the students’ perceptions of their engagement and influencing factors of engagement at a single institution, but they did not assess how often these factors were implemented at that institution.

The current study investigates the engagement strategies used in a large, public, research institution, students’ opinions about these course methods, and students’ overall perception of learning in-person versus during ERT. This study aims to answer the following questions:

1. How has the change from in-person to online learning affected student attendance, performance expectations of students, and participation in lectures?

2. What engagement tools are being utilized in lectures and what do students think about them?

3. What influence do social interactions with peers, teachers, and administration have on student engagement?

These three questions encompass the three different dimensions of engagement, including multiple facets of each, as well as explicitly highlighting the role of technology in student engagement.

Materials and Methods

Data were collected from two main sources: a survey of undergraduates, and Course and Professor Evaluations (CAPE). The study was deemed exempt from further review by the institution’s Institutional Review Board because identifying information was not collected.

The survey consisted of 50 questions, including demographic information as well as questions about both in-person and online learning (Refer to full survey in Supplementary Material.). The survey, hosted on Qualtrics, was distributed to undergraduate students using various social media channels, such as Reddit, Discord, and Facebook, in addition to being advertised in some courses. In total, the survey was answered by 237 students, of which 187 completed the survey in full, between January 26th and February 15, 2021. It was made clear to students that the data collected would be anonymous and used to assess engagement over the course of Fall 2019 to Fall 2020. The majority of the survey was administered using five-point Likert scales of agreement, frequency, and approval. The survey was divided into blocks, each of which used the same Likert scale. Quantitative analysis of the survey data was conducted using R, and visualized with the likert R package ( Bryer and Speerschneider, 2016 ).

A number of steps were taken to ensure that survey responses were valid. Before survey distribution, 2 cognitive interviews were conducted with undergraduate students attending the institution in order to refine the intelligibility of survey items ( Desmione and Carlson Le Floch, 2004 ). Forty-eight incomplete surveys were excluded. In addition, engagement tests were placed within the larger blocks of the survey in order to prevent respondents from clicking the same choice repeatedly without reading the prompts. The two students who answered at least one of these questions incorrectly were excluded.

Respondent Profile

Respondents were asked before the survey to confirm that they were undergraduate students attending the institution over the age of 18. Among the 187 students that filled out the survey in its entirety, 21.9% were in their first year, 28.3% in their second year, 34.2% in their third year, 11.8% in their fourth year, and 1.1% in their fifth year or beyond. It should be noted, therefore, that some students, especially first-years, had no experience with in-person college education at the institution, and these respondents were asked to indicate this for any questions about in-person learning. However, all students surveyed were asked before participating whether they had experience with online learning at the institution. 2.7% of respondents were first year transfers. 72.7% of overall respondents identified as female, 25.7% as male, 0.5% as non-binary, and 1.1% preferred not to disclose gender. In regards to ethnicity, 45.6% of respondents identified as Asian, 22.8% as White, 13.9% as Hispanic/Latinx, 1.7% as Middle Eastern, 1.6% as Black or African-American, and 2.2% as Other. 27.9% of respondents were first-generation college students, 7.7% of respondents were international students, and 9.9% of students were transfer students.

In the most recent report for the 2020–2021 academic year, the Institutional Research Department noted that out of 31,842 undergraduates, 49.8% of undergraduates are women and 49.4% are men ( University of California, San Diego Institutional Research, 2021 ). This report states that 17% of undergraduates are international students, which is a larger percentage than is represented by survey respondents ( University of California, San Diego Institutional Research, 2021 ). The institution reports 33% of undergraduates are transfer students, which are also underrepresented in the survey respondents ( University of California San Diego [UCSD], 2021b ). The ethnicity profile of the survey respondents is similar to the undergraduate student demographic at this institution. According to the institutional research report, among undergraduates, 37.1% are Asian American, 19% are White, 20.8% are Chicano/Latino, 3% are African American, 0.4% are American Indian, and 2.5% are missing data on ethnicity ( University of California, San Diego Institutional Research, 2021 ).

Course and Professor Evaluation Reviews

Data were also collected from the institution’s CAPE reviews, a university-administered survey offered prior to finals week every quarter, in which undergraduate students are asked to rate various aspects of their experience with their undergraduate courses and professors ( Courses not CAPEd for Winter 22, 2022 ). CAPE reviews are anonymous, but are sometimes incentivized by professors to increase participation.

Although it was not designed with Bond and Bedenlier’s student engagement framework in mind, the questions on the CAPE survey still address the fundamental influences on engagement established by the framework. The CAPE survey asks students how many hours a week they spend studying outside of class, the grade they expect to receive, and whether they recommend the course overall. The survey then asks questions about the professor, such as whether they explain material well, show concern for student learning, and whether the student recommends the professor overall.

In this study, we chose to look only at data from Fall 2019, a quarter where education was in-person, and Fall 2020, when courses were online. In Fall 2019, there were 65,985 total CAPE reviews submitted, out of a total of 114,258 course enrollments in classes where CAPE was made available, for a total response rate of 57.8% ( University of California San Diego [UCSD], 2021a ). The mean response rate within a class was 53.1% with a standard deviation of 20.7%. In Fall 2020, there were 65,845 CAPE responses out of a total of 118,316 possible enrollments, for a total response rate of 55.7%. The mean response rate within a class was 50.7%, with a standard deviation of 19.6%.

In order to adjust for the different course offerings between quarters, and for the different professors who might teach the same course, we selected only CAPE reviews for courses that were offered in both Fall 2019 and Fall 2020 with the same professors. This dataset contained 31,360 unique reviews (16,147 from Fall 2019 and 15,213 from Fall 2020), covering 587 class sections in Fall 2019 and 630 in Fall 2020. Since no data about the students were provided with the set, however, we do not know how many students these 31,360 reviews represent. This pairing strategy offers many interesting opportunities to compare the changes and consistencies of student reviews between both quarters in question. To keep this study focused on the three research questions and in observation of time and space limitations, analysis was only performed on the pairwise level of the general CAPE survey questions and not broken down to further granularity.

The CAPE survey was created by the designers of CAPE, not the researchers of this paper. The questions on the CAPE survey are general and only provide a partial picture of the status of student engagement in Fall 2019 and Fall 2020. The small scale survey created by this research team attempts to clarify and make meaning of the results from the CAPE data.

Data Analysis

Survey data.

Survey data was collected and exported from Qualtrics as a. csv file, then manually trimmed to include only relevant survey responses from participants who completed the survey. Data analysis was done in R using the RStudio interface, with visualizations done using the likert and ggplot2 R packages ( Bryer and Speerschneider, 2016 ; Wickham, 2016 ; R Core Team, 2020 ; RStudio Team, 2020 ). Statistical tests were performed on lecture data, using paired t -tests, and Mann–Whitney U tests of the responses; for example, when comparing attendance of in-person lectures in Fall 2019 and live online lectures on Zoom in Fall 2020.

Course and Professor Evaluation Data

As previously mentioned, analysis of CAPE reviews was restricted to courses that were offered in both Fall 2019 and 2020 with the same professor, with Fall 2019 courses being in-person and Fall 2020 courses being online. This was done since the variation of interest is the change from in-person to online education, and restricting analysis to these courses allowed the pairing of specific courses for statistical tests, as well as the adjustment for any differences in course offerings or professor choices between the two quarters. In order to compare ratings for a specific item, first, negative items were recoded if necessary. The majority of questions were on a 5-point Likert scale, though some, such as expected grade, needed conversion from categorical (A–F scale) to numerical (usually 0–4). Then, the two-sample Mann–Whitney U test was conducted on the numerical survey answers, comparing the results from Fall 2019 to those from Fall 2020. Results were then visualized using the R package ggplot2 ( Wickham, 2016 ), as well as the likert package ( Bryer and Speerschneider, 2016 ).

In this study, we aimed to take a broad look at the state of online learning at UCSD as compared to in-person learning before the COVID-19 pandemic. This assessment was split into three general categories: changes in lecture engagement and student performance, tools that professors and administrators have implemented in the face of online learning, and changes in patterns of students’ interactions with their peers and with instructors. In general, while we found that students’ ratings of their professors and course staff remained positive, there were significant decreases in lecture engagement, attendance, and perceived ability to keep up with coursework, even as expected grades rose. In addition, student-student interactions fell for the vast majority of students, which students felt hurt their learning experience.

Course and Professor Evaluation Results

How has the change from in-person to online learning affected student attendance, performance expectations, and participation in lectures, lecture attendance.

In the CAPE survey, students reported their answers to a series of questions relating to lecture attendance and engagement. Table 1 reports the results of the Mann–Whitney U test for each question, in which the results from Fall 2019 were compared to the results from Fall 2020. Statistically significant differences were found between students’ responses to the question “How often do you attend this course?” (rated on a 1–3 scale of Very Rarely, Some of the Time, and Most of the Time), although students were still most likely to report that they attended the class most of the time. Statistically significant decreases were also found for students’ agreement to the questions “Instructor is well-prepared for classes,” and “Instructor starts and finishes classes on time.” It should be noted that “attendance” was not clarified as “synchronous” or “asynchronous” attendance to survey respondents.

Table 1. Mean and standard deviations of student responses on CAPE evaluation questions relating to lecture attendance and engagement in Fall 2019 and 2020.

Expected Grades

Within the CAPE survey, students are asked, “What grade do you expect in this class?” The given options are A, B, C, D, F, Pass, and No Pass. The proportion of CAPE responses in which students reported taking the course Pass/No Pass stayed relatively constant from Fall 2019 to Fall 2020, going from 6.5% in Fall 2019 to 6.4% in Fall 2020. As can be seen in Figure 1 , participants were more likely to expect A’s in Fall 2020; in Fall 2019, the median expected grade was an A in 56.8% of classes, while in Fall 2020, this figure was 68.0%. We used a Mann–Whitney U test to test our hypothesis that there would be a difference between Fall 2019 and Fall 2020 expected grades because of students’ and instructors’ unfamiliarity with the online modality. When looking solely at classes in which students expected to receive a letter grade, after recoding letter grades to GPA equivalents, a significant difference was found between expected grades in Fall 2019 and 2020, with a mean of 3.443 in FA19 and 3.538 in FA20 ( U = 92286720, p < 0.001).

Figure 1. Distribution of grades expected by students prior to finals week in CAPE surveys in Fall 2019 and Fall 2020.

What Engagement Tools Are Being Utilized by Professors and What do Students Think About Them?

Assignments and learning.

As part of the CAPE survey, respondents were asked to rate their agreement on a 5-point Likert scale to questions about their assignments and learning experience in the class. Results are displayed in Table 2 . Statistically significant increases in student agreement, as indicated by the two-sample Mann–Whitney U test, were reported in the questions “Assignments promote learning,” “The course material is intellectually stimulating,” and “I learned a great deal from this course.”

Table 2. Student responses on CAPE evaluation statements relating to assignments, course material, and quality of learning.

What Influence do Social Interactions With Peers, Teachers, and Administration Have on Student Engagement?

Professor efficacy and accessibility.

As part of the CAPE survey, students also rated their professors in various aspects, as can be seen in Table 3 . The only significant result observed between Fall 2019 and Fall 2020 was a slight increase in student agreement with the statement “Instructor is accessible outside of class.”

Table 3. Student responses on CAPE evaluation statements relating to instructor efficacy and accessibility.

Survey Results

General satisfaction.

Respondents were asked to indicate their agreement on a 5-point Likert scale (Strongly Disagree, Disagree, Neither Agree nor Disagree, Agree, and Strongly Agree) to the statement, “In general, I am satisfied with my online learning experience at [institution].” 36% of respondents agreed with the statement, 28% neither agreed nor disagreed, and 36% disagreed.

Perceptions of Academic Performance

Students were asked to rate their agreement on a 5-point Likert scale of agreement to a series of broad questions about their online learning experience, some of which pertained to academic performance. When assessing the statement “My current online courses are more difficult than my past in-person courses,” 42% chose Strongly Agree or Agree, 32% chose Neither Agree nor Disagree, and 26% chose Disagree or Strongly Disagree. Respondents were also split on the statement “My academic performance has improved with online education,” which 28% agreed/strongly agreed with, 34% disagreed/strongly disagreed with, and 38% chose neither.

For the statement “I feel more able to manage my time effectively with online education than with in-person education,” only 34% agreed/strongly agreed with the statement while 45% disagreed/strongly disagreed and 21% chose neither. For the statement, “I feel that it is easier to deal with the pace of my course load with online education than with in-person education,” 30% of respondents agreed/strongly agreed, 54% disagreed/strongly disagreed, and 16% neither agreed nor disagreed.

Lecture Attendance by Class Type

Since the CAPE survey question regarding attendance did not specify asynchronous or synchronous attendance, students were asked on the survey created by the authors of this paper how often they attended and skipped certain types of lectures. In response to the question “During your last quarter of in-person classes, how often did you skip live, in-person lectures?,” 11% reported doing so often or always, 14% did so sometimes, and the remaining 74% did so rarely or never. The terms “Sometimes” and “Rarely” were not clarified to the respondents. This is the same scale and language used on the CAPE survey, however, which was a benefit to synthesizing and comparing this data with CAPEs. Meanwhile, for online classes, 35% reported skipping their live classes often or always, 23% did so sometimes, and 43% did so rarely or never.

Respondents were also asked about their recorded lectures, both in-person and online; while some courses at the institution are recorded and released in either audio or video form for students, most online synchronous lectures are recorded. When asked how often they watched recorded lectures instead of live lectures in-person, 12% of respondents said they did so often or always, 12% reported doing so sometimes, and 76% did so rarely or never. For online classes where recorded versions of live lectures were available, 47% of students reported watching the recorded version often or always, 21% did so sometimes, and 33% did so rarely or never.

Meanwhile, there were also some lectures during online learning that were offered only online (asynchronous), as opposed to being recorded versions of lectures that were delivered to students live over Zoom.

Students were asked questions about their lecture attendance for in-person learning pre-COVID and for online learning during the pandemic. On a 5 point Likert scale from Never to Always, 11% of students said they skipped “live, in-person lectures” in their courses pre-COVID Often or Always. On the same scale, 35% of respondents said they skipped live online lectures Often or Always. To assess the significance of these reports, we conducted a one-sided Mann–Whitney U test with the null hypothesis that the median frequency of students skipping live online lectures is greater than the median frequency of skipping live in-person lectures. Previous research suggesting that lecture attendance decreased after the COVID-19 transition motivated our alternative hypothesis that students would skip live online lectures more often ( Perets et al., 2020 ). The result was significant, meaning that this evidence suggests that students skip online lectures (Mdn = 3 “Sometimes”) more often than live in-person lectures (Mdn = 2 “Rarely”), U = 23328, p < 0.001. The results were also significant when a one-sided 2 sample t -test was performed to test if students were skipping online lectures ( M = 2.84, SD = 1.13) more often than they skipped in-person lectures ( M = 1.97, SD = 1.06), t (358.53) = 7.55, p < 0.001.

In order to clarify why students might be skipping lectures, we asked students how often they were using the recorded lecture options during in-person and online learning. 12% of respondents reported that they watched the recorded lecture “Often” or “Always” instead of attending the live lecture in-person while 47% of respondents said that they watched the recorded version of lecture, if it was offered, “Often” or “Always” rather than the live version during remote learning. When a one-sided Mann–Whitney U test was performed comparing the medians of students that utilized the recorded option during in-person classes (Mdn = 2 “Rarely”) and during online classes (Mdn = 3 “Sometimes”), the results were significant, suggesting that more students watch a recorded lecture version when it is offered during online classes, U = 6410, p < 0.001. The results are also significant with a t -test comparing the means of students that watched the recorded format during in-person classes ( M = 1.95, SD = 1.08) and during online classes ( M = 3.23, SD = 1.23), t (330.84) = –10.13, p < 0.001.

Students were asked how often they used course materials, such as a textbook or instructor provided notes and slideshows, rather than attending a live or recorded lecture to learn the necessary material. 10% of students said that they used course materials “Often” or “Always” during in-person learning while 19% of students said they used course materials “Often” or “Always” during online learning. The results were significant in a one-sided Mann–Whitney U test for the null hypothesis that the medians are equivalent for students using materials during in-person learning (Mdn = 1 “Never”) and during online learning (Mdn = 2 “Rarely”), U = 12644, p < 0.001. In other words, the evidence suggests that students use course materials instead of attending lectures more often when classes are online than when classes are in-person. A one-sided t -test also indicates that students during online learning ( M = 2.30, SD = 1.16) utilize provided materials instead of watching lecture to learn course material more often than students during in-person learning ( M = 1.76, SD = 1.03), t (364.55) = –4.72, p < 0.001.

Discussions are supplementary and sometimes mandatory classes to the lecture conducted by a teaching assistant. Students reported that during the last quarter of online classes the discussion sections tended to include synchronous live discussion instead of pre-recorded content (see Table 4 ).

Table 4. Distribution of survey responses to questions about non-mandatory discussion sections.

Reported Attendance and Engagement in Lecture

Students were asked to rate their agreement on the same 5-point Likert scale to a series of questions about their in-lecture attendance and engagement. When presented with the statement “I feel more comfortable asking questions in online classes than in in-person ones,” 56% of students agreed, 22% neither agreed nor disagreed, and 22% disagreed. Here, “agreed” includes strongly agree and disagree includes “strongly disagreed.” This was similar to the result for “I feel more comfortable answering questions in online classes than in in-person ones,” to which 56% agreed, 24% neither agreed nor disagreed, and 20% disagreed.

When students who had taken both in-person and online courses were directly asked about overall attendance of live lectures, with the statement “I attend more live lectures now that they are online than I did when lectures were in-person,” 12% agreed, 19% neither agreed nor disagreed, and 69% disagreed (with 32.5% selecting “Strongly disagree”).

Issues With Online Learning

Respondents were asked to indicate on a 5-point Likert frequency scale (Never, Rarely, Sometimes, Often, and Always) how often a series of possible issues affected their online learning. These are reported in Figure 2 . The most common technical issue was unreliable WiFi. 20% of students say unreliable WiFi happens “Often” or “Always,” 35% say this issue happens to them “Sometimes,” and 45% of students say unreliable WiFi affects their online learning “Never” or “Rarely.” The next common technological problem students face is unreliable devices. A poor physical environment affected students’ online learning for 32% of the respondents “Often” or “Always.” Issues with platforms, such as Gradescope, Canvas, and Zoom, were present but reported less often.

Figure 2. Prevalence of issues in online education among student survey respondents ( n = 187).

Course Structure

For a given possible intervention in course structure, students were asked how often their professors implemented the changes and to rate their opinion of the learning strategy. The examined changes were weekly quizzes, replacing exams with projects or other assignments, interactive polls or questions during lectures, breakout rooms within lectures, open-book or open-note exams, and optional or no-fault final exams – exams that will not count toward a student’s overall grade if their exam score does not help their grade.

Respondents’ reported frequencies of these interventions are displayed in Figure 3 , and their ratings of them are displayed in Figure 4 . In addition to being the most common intervention, open book exams were also the most popular intervention among students, with 89% of respondents reporting that they had a Good or Excellent opinion. Similarly popular were in-lecture polls, optional finals, and replacing exams with assignments, while breakout sessions had a slightly negative favorability.

Figure 3. Students’ reported frequencies of certain possible interventions in online learning.

Figure 4. Students’ reported approval of certain possible interventions in online learning.

Academic Tools and Resources

In the survey, students were asked to rate their agreement with the statement, “Online learning has made me more likely to use academic resources such as office hours, tutoring, or voluntary discussion sessions.” 42% of students agreed (includes “strongly agreed”), 23% neither agreed nor disagreed, and 35% disagreed (includes “strongly disagreed”). However, for the statement, “Difficulties accessing office hours or other academic resources have negatively interfered with my academic performance during online education,” 26% of students agreed/strongly agreed, 24% neither agreed nor disagreed, and 49% disagreed/strongly disagreed.

Respondents were asked to rate their opinion of various academic resources on a 5-point scale (Terrible, Poor, Average, Good, and Excellent) for both in-person and online classes ( Figures 5 , 6 ). The most notable change in rating was for the messaging platform Discord, which 67% of respondents saw as a Good or Excellent academic resource during online education, compared to 34% in in-person education. The learning management system Canvas also saw an increase in favorability, while favorability decreased for course discussions.

Figure 5. Students’ reported approval ratings of certain academic resources and tools when classes were in-person.

Figure 6. Students’ reported approval ratings of certain academic resources and tools when classes were online.

Respondents were asked to rate the frequency at which they and their professors turned their cameras on during lectures. 64% of students reported keeping their cameras on never or rarely, 29% reported keeping cameras on sometimes, and 6% of students reported keeping their cameras on often or always. Meanwhile, for professors, 58% of students reported that all of their professors kept their cameras on, 28% said most kept their cameras on, 9% said about half did so, and the remaining 5% said that some or none of their professors kept cameras on.

Personal Interaction

A lack of social interaction was among the largest complaints of students about online learning. 88% of respondents at least somewhat agreed with the statement “I feel less socially connected to my peers during online education than with in-person education.” When students were asked how often certain issues negatively impacted their online learning experience, 64% of respondents indicated that a lack of interaction with peers often or always impacted their learning experience, and 44% reported the same about a lack of instructor interaction.

When we asked students how they stay connected to their peers, 78.6% said that they stay connected to peers through student-run course forums, such as Discord, a messaging app that is designed to build communities of a common interest. 72.7% said they use personal communication, i.e., texting, with peers. 48.1% of students said they use faculty-run course forums, such as Piazza or Canvas. 45.5% of students surveyed keep in touch with peers through institution clubs and organizations. 29.4% of students selected that they use student-made study groups and 19.8% stay connected through their campus job.

Ratings of University Faculty and Staff

Students were asked to rate their opinion of various faculty and staff, by answering survey statements of the form “____ have been sufficiently accommodating of my academic needs and circumstances during online learning.” For instructors, 72% agreed/strongly agreed with this statement and 11% disagreed/strongly disagreed; for teaching assistants and course tutors, 81% agreed/strongly agreed, and only 2% disagreed/strongly disagreed. Meanwhile, for university administration, 39% of students agreed/strongly agreed, 34% neither agreed nor disagreed, and 26% disagreed/strongly disagreed.

Based on both the prior literature and this study, students seemed to struggle with engagement before the pandemic during in-person lectures, and it appears from the survey findings that students are struggling even more with engagement in online courses. A U.S. study investigating the teaching and learning experiences of instructors and students during the COVID-19 pandemic also found that when learning transitioned online, students’ main issue was engagement whereas prior to the pandemic the main issue for students was content ( Perets et al., 2020 ). The lack of peer connection and technological issues seem to be significant problems for students during online learning and could contribute to students’ issues with engagement. The problems with attention during an online lecture might be attributed to the lack of social accountability that an in-person lecture promotes to put away distractions like cell phones and taking active notes. Additionally, CAPE data shows that students rate their professors’ efforts and course design highly and similarly before and during Fall 2019 and Fall 2020. Although every course and professor has different requirements, creating collaborative opportunities and incorporating interactive features into lectures could be beneficial to student engagement.

For live lectures, the increase in students reporting skipping live online lectures more often may be due to the increase in availability and ease of recorded options with online lectures. A similar study to this research found that when the university transitioned to Pass/No Pass grading rather than letter grading during ERT, students attended synchronous lectures less ( Perets et al., 2020 ). During the pandemic, the institution’s deadline to change to P/NP grading was extended and more academic departments allowed Pass/No Pass classes to fulfill course requirements. In our study, we did not detect an increase in students who took advantage of the P/NP grading, but it is possible that students skipped more synchronous lectures knowing that they could use the Pass option as a safety net if they did not dedicate the typical amount of lecture time to learn the material. The results emphasize the vital role of the cognitive dimension in engagement.

It is clear that more students are taking advantage of recorded options with online learning. A survey of Harvard medical students indicates a preference for the recorded option because of the ability to increase the speed of the lecture video and prevent fatigue ( Cardall et al., 2008 ). Consistent with previous research, our results suggest that students may seek more value and time management options from course material when classes are fully online ( Perets et al., 2020 ). Recorded lectures allow freedom for students to learn at a time that works best for them ( Rae and McCarthy, 2017 ). For discussions, students reported that they had more discussions that were live rather than recorded. Research indicates that successful online learning requires strong instructor support ( Dixson, 2010 ; Martin and Bolliger, 2018 ). The smaller class setting of a discussion, even virtual, may promote better engagement through interaction among the students, content, and the discussion leader.

Based on CAPE results, which are conducted the week before final exams, students expected higher grades during the online learning period. Although expected grades rose, students concur with previous surveys that the workload was overwhelming and was not adequately adjusted to reflect the circumstances of ERT ( Hussein et al., 2020 ; Shin and Hickey, 2020 ). While there are many factors that could account for this, including the fact that expected grades reported on CAPE do not reflect a student’s actual grade, one possible explanatory factor is the use of more lenient grading standards and course practices during the pandemic. In addition to relaxed Pass/No Pass standards, courses were more likely to adopt practices like open-book tests or no-fault finals, providing students with assessments that emphasized a demonstration of deeper conceptual understanding rather than memorization. It is important to note that students’ perceptions of their learning does not indicate that students are actually learning or performing better academically. This goes for the CAPE question, “I learned a great deal from this course,” the CAPE question about expected grades, and the small scale survey reports about academic performance. We took interest in these questions because they offer insight into the level of difficulty students perceived during ERT due to the shift in engagement demands from remote learning. More research should be done with students’ academic performance data before and after ERT to clarify whether there was a change in students’ learning.

Students’ preference for using a virtual platform during lecture to ask, answer, and respond to questions was surprising. This extends previous evidence from Vu and Fadde (2013) , who found that, in a graduate design course at a Midwestern public university with both in-person and online students in the same lecture, students learning online were more likely to ask questions through a chat than students attending in-person lectures. In addition, during the COVID-19 pandemic, Castelli and Sarvary (2021) report that Zoom chat facilitates discussions for students, especially for those who may not have spoken in in-person classes.

When students were surveyed on the issues they faced with online learning, the most common issues had to do with engagement in lectures, interaction with instructors and peers, and having a poor physical work environment, while technical issues or issues with learning platforms were less common. The distinction between frequency and impact is key, since issues such as bad WiFi connection can be debilitating to online learning even if uncommon, and issues with technology and physical environment also correlate with equity concerns. Other surveys have found that students and faculty from equity-seeking groups faced more hardships during online learning because of increased home responsibilities and problems with internet access ( Chan et al., 2020 ; Shin and Hickey, 2020 ). Promoting student engagement in class involves more than well-planned teaching strategies. Instructors and universities need to look at the resources and accessibility of their class to reduce the digital divide.

According to the CAPE data from Table 2 , instructors received consistent reviews before and after the ERT switch, indicating that they maintained their effectiveness in teaching. The ratings for two CAPE prompts “Instructor is well prepared for class” and “Instructor starts and finishes class on time” had statistically significant decreases from Fall 2019 to Fall 2020. This decrease could be attributed to increased technological preparation needed for online courses and the variety of offerings for lecture modalities. For example, some instructors chose to offer a synchronous lecture at a different time than the original scheduled course time, and then provide office hours during their scheduled lecture time to discuss and review the lectures. Regardless of the statistically significant changes, the means for these two statements are high and similar to Fall 2019.

What Engagement Tools Are Being Utilized in Lectures and What do Students Think About Them?

Based on the results, a majority of students report that their professors are using weekly quizzes, breakout rooms, and polls at least sometimes in their classes to engage students. Students had highly positive ratings of in-course polling, were mostly neutral or positive about weekly quizzes (as a replacement for midterm or final exams), but were slightly negative about breakout rooms. Venton and Pompano (2021) report positive qualitative student feedback from students in chemistry classes at the University of Virginia, with some students finding it easier to speak up and make connections with peers than in an entire class; Fitzgibbons et al. (2021) , meanwhile, found in a sample of 15 students at the University of Rochester that students preferred working as a full class instead of in breakout rooms, though students did report making more peer connections in breakout rooms. Breakouts have potential to strengthen student-student and student-instructor relationships, but further research is needed to clarify their effectiveness.

Changes were also made to course structure, with almost all (94%) of students reporting that open-book exams were used at least sometimes. Open-book exams were also the most popular intervention overall, although the reason for their widespread adoption (academic integrity and fairness concerns) is likely different from the reasons that students like them (less focus on memorization). Open-book tests, however, present complications. Bailey et al. (2020) notes that while students still needed a good level of understanding to succeed on open-book exams, these exams were best suited to higher-order subjects without a unique, searchable answer.

Changes were detected in the responses to the CAPE statements, “Assignments promote learning,” “The course material is intellectually stimulating,” and “I learned a great deal from this course,” noted in Table 3 . Although there were statistically significant changes detected by the Mann–Whitney U Test, the means between Fall 2019 and Fall 2020 are still similar and positive. The results from this table indicate that students felt that there was not a decrease in learning and interest in their material. This might be due to instructors changing the design of assessments and assignments to accommodate for academic integrity and modality circumstances in the online learning format. The consistently positive CAPE ratings are also likely due to the fact that students are aware that CAPEs are an important factor for the departments’ hiring and retention decisions for faculty, and subsequently important for their instructors’ careers. Students may have also recognized that most of the difficulties in the switch to online learning were not the instructors’ fault. Students’ sympathy for the challenges that instructors faced may be contributing to the slightly more positive reviews during Fall 2020.

One of the most common experiences reported by students was a decrease in interaction with peers, with a strong majority of students saying that a lack of peer interaction hurt their learning experience. A study from Central Michigan University shows that peer interaction through in class activities supports optimal active learning ( Linton et al., 2014 ). Without face-to-face learning and asynchronous classes during COVID, instructors were not able to conduct the same collaborative activities. When asked how students interacted with their peers, the most common responses were student-run course forums or texting. This seems to support the findings of Wong (2020) which indicated that during ERT, students largely halted their use of synchronous forms of communication and opted instead for asynchronous ones, like instant messaging, with possible impacts on students’ social development. Students also reported a decrease in interaction with their instructors with a plurality saying that a lack of access to their instructors affected their academic experience. At the same time, ratings of professors’ ability to accommodate for the issues students faced during online education were high, as were students’ ratings of online office hours. It seems that students sympathized with instructors’ difficulties in the ERT transition but were aware that the lack of instructor presence impacted their learning experience nonetheless.

Limitations

There are some limitations in this study that should be considered before generalizing the results more widely. The survey was conducted at just a single university, UCSD: a large, highly-ranked, public research institution in the United States with its own unique approach to the COVID-19 pandemic. These results would likely differ significantly for online education at other universities. In addition, though care was taken to distribute the survey in channels used by all students, the voluntary response of students chosen from these channels does not constitute a simple random sample of undergraduates attending this institution. For example, our survey over-represents female students, who constituted 72.7% of the survey sample. The channels chosen could also bias certain results; for example, it is possible that students who answer online surveys released on the institution’s social media channels are less likely to have technical or Internet difficulties. Results from the small survey might be skewed slightly because respondents had to recall a year prior to their experiences in Fall 2019, whereas they might have had a more accurate memory of their Fall 2020 experience. CAPEs are completed at the end of the quarter when their recollection of their experiences is fresh, so those reviews are likely less susceptible to this unconscious bias.

The issues with sampling are somewhat mitigated in the CAPE data, but these responses are not themselves without issue. CAPE reviews are still a voluntary survey, and therefore are not a random sample of undergraduates. In addition, some instructors use extra credit to incentivize students to participate in CAPEs if the class meets a threshold percentage of responses, which might skew the population of respondents. CAPE responses tend to be relatively generous and positive, with students rating instructors and educational quality much higher in CAPE reviews than in our survey. This is possibly because the CAPE forms make it easy for students to report the most positive ratings on every item without considering them individually. Additionally, students are aware that CAPEs have an impact on the department’s decisions to rehire instructors.

Teaching Implications

Online learning presented multiple challenges for instructors and students, illuminating areas to improve in higher education that were not recognized before the COVID-19 pandemic. A majority of students expressed their comfort in engaging with the Zoom chat and polling. Students might feel this way since they can ask and answer questions using the chat feature without disrupting the focus in class. Therefore, in both further online learning and in-person classes, instructors might be able to stimulate interaction by lowering the social barriers to asking and answering questions. Applications such as Backchannel Chat, Yo Teach!, and NowComment offer more features than Zoom or Google Meet to prevent fatigue and increase retention in-person or online ( LearnWeaver, 2014 ; Hong Kong Polytechnic University, 2018 ; Paul Allison, 2018 ).

At the same time, increased interactivity in lectures, especially if required, is not necessarily a panacea for engagement issues. For example, some professors might require students to turn on their cameras, increasing accountability and giving an incentive to visibly focus as if in an in-person classroom. However, Castelli and Sarvary (2021) found, as we did, that the majority of students in an introductory collegiate biology course kept their cameras off; students cited concerns about their appearance, other people being seen behind them, and weak internet connections as the most common reasons for not keeping cameras on. Not only are these understandable concerns, but they correlate with identity as well: Castelli and Sarvary found that both underrepresented minorities and women were more likely to indicate that they worried about cameras showing others their surroundings and the people behind them.

Prior to the COVID-19 pandemic, online learning was a choice. Our research demonstrates that online learning has a long way to go before it can be used in an equitable manner that creates an engaging environment for all students, but that instructors adapted well to ERT to ensure courses promoted the same level of learning. The sudden nature of remote learning during the COVID pandemic did not allow for instructors or institutions to research and promote the most engaging online learning resources. Students have widely varying opinions and experiences with their higher education online learning experience during the pandemic. Our data analysis shows that distance learning during the pandemic had a toll on attendance during live lecture and peer-instructor connection. The difference in expected grades from Fall 2019 to Fall 2020 indicates that students felt differently about their ability to succeed in their online classes. In addition, students had trouble managing work loads during online learning. We gathered that instructors could be using engagement strategies more often to match students’ enthusiasm for those strategies, such as chat features and polls. Despite the challenges of online learning highlighted, this research also presents evidence that online learning can be engaging for students with the right tools. Student reviews indicated similarity before and after the switch to online learning, including indicating that course assignments promoted learning and the material was intellectually stimulating. These results propose that the courses and professors, despite the modality switch and changes to teaching and assessment strategies, maintained the level of learning that students felt they were getting out of their course.

Data Availability Statement

The data supporting the conclusions of this article contains potentially identifiable information. The authors can remove this identifying information prior to sharing the data.

Author Contributions

BH contributed to this project through formal analysis, investigation, and writing. PN contributed to the project through formal analysis, investigation, visualization, and writing. LC contributed to conceptualization, resources, supervision, writing, review, and editing. SH-L contributed methodology, supervision, writing, review, and editing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would like to acknowledge the Qualcomm Institute Learning Academy for supporting this project.

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Keywords : student engagement, undergraduate, online learning, in-person learning, remote instruction and teaching

Citation: Hollister B, Nair P, Hill-Lindsay S and Chukoskie L (2022) Engagement in Online Learning: Student Attitudes and Behavior During COVID-19. Front. Educ. 7:851019. doi: 10.3389/feduc.2022.851019

Received: 08 January 2022; Accepted: 11 April 2022; Published: 09 May 2022.

Reviewed by:

Copyright © 2022 Hollister, Nair, Hill-Lindsay and Chukoskie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Brooke Hollister, [email protected]

† These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Open Access

Peer-reviewed

Research Article

The impact of the COVID-19 pandemic on higher education: Assessment of student performance in computer science

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Computer Science, Lublin University of Technology, Lublin, Poland, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Roles Conceptualization, Formal analysis, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Computer Science, Lublin University of Technology, Lublin, Poland

Roles Data curation, Software

• Małgorzata Charytanowicz, 
• Magdalena Zoła, 
• Waldemar Suszyński

• Published: August 14, 2024
• https://doi.org/10.1371/journal.pone.0305763
• Reader Comments

The COVID-19 pandemic had radically changed higher education. The sudden transition to online teaching and learning exposed, however, some benefits by enhancing educational flexibility and digitization. The long-term effects of these changes are currently unknown, but a key question concerns their effect on student learning outcomes. This study aims to analyze the impact of the emergence of new models and teaching approaches on the academic performance of Computer Science students in the years 2019–2023. The COVID-19 pandemic created a natural experiment for comparisons in performance during in-person versus synchronous online and hybrid learning mode. We tracked changes in student achievements across the first two years of their engineering studies, using both basic (descriptive statistics, t-Student tests, Mann-Whitney test) and advanced statistical methods (Analysis of variance). The inquiry was conducted on 787 students of the Lublin University of Technology (Poland). Our findings indicated that first semester student scores were significantly higher when taught through online (13.77±2.77) and hybrid (13.7±2.86) approaches than through traditional in-person means as practiced before the pandemic (11.37±3.9, p-value < 0.05). Conversely, third semester student scores were significantly lower when taught through online (12.01±3.14) and hybrid (12.04±3.19) approaches than through traditional in-person means, after the pandemic (13.23±3.01, p-value < 0.05). However, the difference did not exceed 10% of a total score of 20 points. With regard to the statistical data, most of the questions were assessed as being difficult or appropriate, with adequate discrimination index, regardless of the learning mode. Based on the results, we conclude that we did not find clear evidence that pandemic disruption and online learning caused knowledge deficiencies. This critical situation increased students’ academic motivation. Moreover, we conclude that we have developed an effective digital platform for teaching and learning, as well as for a secure and fair student learning outcomes assessment.

Citation: Charytanowicz M, Zoła M, Suszyński W (2024) The impact of the COVID-19 pandemic on higher education: Assessment of student performance in computer science. PLoS ONE 19(8): e0305763. https://doi.org/10.1371/journal.pone.0305763

Editor: Prabhat Mittal, Satyawati College (Eve.), University of Delhi, INDIA

Received: October 15, 2023; Accepted: June 4, 2024; Published: August 14, 2024

Copyright: © 2024 Charytanowicz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available at the following link: https://zenodo.org/records/11583297 .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The COVID-19 pandemic brought with it a number of health, economic and social consequences. Indeed, the spread of the SARS-CoV-2 virus turned out to be so dangerous that many countries implemented new regulations in the educational field to limit physical contact. The pandemic-induced school shutdowns and sudden transition to remote teaching and learning at all levels of education. This change-over generated a number of technical and social problems [ 1 – 6 ]. These problems had also affected the academic community, although online or blended learning methods were implemented before the COVID-19 pandemic [ 7 ].

On March 12, 2020, a state of epidemic emergency was declared in Poland, and a week later–a state of pandemic. In consequence, the Minister of Science and Higher Education issued a regulation on the temporary suspension of the functioning of education institutes, lasting from March 12 till 25 2020 [ 8 , 9 ]. On March 25, 2020, the education system, including higher education, was switched to online teaching and learning, as necessitated by the need to maintain social distancing measures. Universities had to adapt to the circumstances almost overnight. However, many universities were not fully prepared with regard to technical capabilities, educational resources and the skills of the teaching staff in organizing distance education [ 10 – 12 ]. Before the COVID-19 pandemic, the applicable regulations of the Ministry of Science and Higher Education did not encourage the authorities of most universities to invest in technologies for conducting fully remote studies. Poland was, however, not an exception in this respect. Many old, prestigious universities in Europe were also reserved about remote learning, and the virtual learning environment was mainly used as a teaching aid.

Fortunately, the information revolution had by this time developed more flexible approaches to learning with the form of Information and Communication Technology (ICT). Indeed, it is one of the leading factors that affect current teaching methodology [ 13 – 18 ]. E-learning systems, their accessibility and functionality, have provided new possibilities to acquire knowledge and to ease the burden of learning. As an outcome, remote teaching and learning are often seen as promising solutions that offer high flexibility and a learner-centered approach that enables students to learn at their own pace [ 19 , 20 ]. Thus, the role of the teacher in the classroom has transformed from that of being the font of knowledge, to an instructional manager identifying relevant resources and creating collaborative learning opportunities. Moreover, online assessments have become increasingly important and now represent one of the most critical aspects of the educational process. Unfortunately, the role of ICT in higher education is still somewhat controversial.

The extreme situation caused by the COVID-19 pandemic provided an opportunity to revise our approach both to traditional and online learning, yet also posing challenges for the future of education systems. The main question of our research was whether the sudden transition to online teaching and learning caused by the COVID-19 pandemic had a negative impact on students academic performance and upon the reliability of the assessment process. We believe that our study can help to reduce the controversies related to remote learning and teaching.

2. Related works

Before the year 2020, the principal recipients of remote education were adults participating in professional development courses [ 21 ]. The COVID-19 pandemic outbreak, however, resulted in increased interest in methods of education that do not require physical meeting between students and teachers. The closure of educational institutions to mitigate the spread of COVID-19 compelled schools and universities to find alternative ways of continuing their operations. This led to the widespread adoption of online learning (e-learning).

The use of e-learning platforms has enabled the transformation of the traditional model of education in which the lecturer transmitted knowledge, into a model of supervised self-education. A separate line of research has been dedicated to the impact of remote education on university students, who are predominantly young adults, and, as such, are less subject to parental supervision. Topics under study include student attitudes towards distance learning [ 22 , 23 ], the technologies and learning platforms utilized [ 24 – 26 ], and the impact of network quality on the smoothness of classes [ 22 , 27 ].

A relatively well researched aspect of e-learning is the analysis of its advantages and disadvantages in comparison to traditional learning [ 28 – 30 ], including its application during the COVID-19 pandemic [ 31 – 34 ]. Undoubtedly, remote education has its benefits, among others, flexibility, speed, time savings [ 35 , 36 ], as well as better use of the infrastructure and organizational savings for the institution [ 37 ]. Distance learning in the form of e-learning also comes with drawbacks, for example, limited interpersonal contacts [ 38 ], lack of immediate feedback [ 39 , 40 ], and problems with self-discipline and adaptability [ 41 – 43 ]. Considering its strengths and weaknesses, e-learning can be viewed as either a replacement or augmentation of traditional approaches to education.

An integral part of remote education is the verification of its results. The topic was covered in literature in the pre-COVID era [ 44 – 46 ], but much less so during the pandemic [ 47 , 48 ]. Our work focuses on the analysis of student performance under the e-learning setup during COVID-19 related confinement and afterwards. The differentiating characteristic of this paper is the fact that it covers a longer period of time, unlike some other research focusing only on a single academic semester [ 49 ].

The COVID-19 pandemic has provided the opportunity to advance usage of online platforms and digital media, as well as to create new education strategies. It should be noted that most students (and instructors) adapted successfully to online teaching and learning [ 50 , 51 ]. However, certain studies [ 52 – 54 ] have indicated negative student feedback. In the year 2023, education has returned to more traditional teaching/learning approaches after more than two years of online learning.

The outbreak of COVID-19 presented a serious challenge to academic education by enforcing a drastic change in the teaching methods. For this reason, we formulated the following research questions:

• How had the COVID-19 pandemic change applied teaching and learning strategies?
• Did the COVID-19 pandemic have a disruptive effect on the academic performance of students resulting in knowledge deficiency?
• How did the change from in-person to online learning affect the reliability of student assessment?

The rest of the paper is structured as follows. Section 3 presents the context of the study, materials and methods. Section 4 explains the results obtained. Sections 5 and 6 conclude our work and describe limitations and future scope.

3. Materials and methods

3.1. design and context.

The research was conducted in the Department of Computer Science of the Lublin University of Technology in Poland, the largest public technical university in the Lublin voivodship. This was a cross-sectional study carried out among students who were enrolled in the first semester of engineering studies in the academic years 2019/2020, 2020/2021 and 2021/2022 (from October to July). Because of the COVID-19 pandemic, the courses of interest in this study were conducted in different delivery formats (in-person, synchronous online and hybrid).

Traditional in-person course delivery format included lectures and laboratories. The former involved, primarily, oral presentations given to a group of students. A teacher-centered approach to learning was applied with discussion and multimedia presentation, as well as whiteboard or chalkboard visual aids to emphasize important points in the lecture. Moreover, a Learning Management System (Moodle LMS) was incorporated within the lectures to develop, organize, deliver and manage didactic materials and assess the effectiveness of education via tests, surveys or assignments. This tool was also employed to provide discussion forums. The faculty used the activity Quiz as a student self-assessment tool, as well as to determine knowledge and skills.

With regard to laboratory work, practical classes were conducted in programming laboratories for the selected courses. In such a teaching/learning format, we found that most students preferred working alone or conducting discussions with their partners or their neighbors.

All students used online manuals or didactic materials delivered by Moodle LMS. Final exams were held at the University via Moodle LMS through in-person proctoring, as this approach allowed the introduction of a live person to monitor the activity of students in a testing environment.

In the synchronous online course format, students obtained theoretical and practical education entirely online via Microsoft Teams by way of video meetings and Moodle LMS. Meetings in Teams include audio, video and screen sharing. All lectures were delivered synchronously using MS Teams. Practical sessions were conducted through online synchronous video meetings in small student groups. Interaction occurred via the discussion board, while MS Teams was also employed to enable scheduled online consultations. Supporting materials (videos, presentations, tasks to do, quizzes, and other didactic materials) were provided to the students through the Moodle LMS. Final exams were conducted under controlled conditions via Moodle LMS through online live proctoring by accepting screen, video and audio sharing.

The hybrid course delivery format combined in-person and online strategies. Students obtained theoretical education entirely online as synchronous sessions by way of MS Teams and Moodle LMS, whilst practical education was obtained through the traditional in-person format, in small student groups. Final exams were held at the University via Moodle LMS through in-person proctoring.

We analyzed exam scores across the first two years of the engineering studies using anonymous data from the Moodle. The Research Ethics Committee of Lublin University of Technology approved the study (Ethical Approval Reference: 3/2023).

3.2. Course selection

The following criteria were used to select the courses:

• the courses covered algorithms and programming,
• the courses had unchanged objectives and learning outcomes during the investigated period,
• the courses were conducted by the same instructors using to the same tools and methods.

Two compulsory courses met these criteria: 1 –Introduction to Computer Science and 2 –Numerical Analysis Algorithms. Both courses were conducted in the Polish language and they provided fundamental knowledge for all areas of Computer Science learning and skills development. Enrolled students were obligated to complete 30 lesson hours of theory and 30 lesson hours of practical experience within a course length of 15 weeks. In the full-time option, four hours of classes were given each course week, and were distributed into two two-hour sessions. Herein, the first consisted of a master class lecture and the second consisted of an interactive problem-based learning laboratory. In the part-time option, the number of in-person teaching hours was reduced to half and classes were held, on average, twice a month, on Saturday and Sunday.

The Introduction to Computer Science course is taught in the first year and is covered in the first semester. Students who successfully completed the course gained five credits, according to the European Credit Transfer and Accumulation System (ECTS). The intention of the offered course is to provide students with knowledge of standard algorithms and data structures, and to provide them with the skills to analyze both the theoretical complexity of algorithms and their practical behaviors. The course covers the following topics:

• Introduction to algorithms and problem-solving techniques.
• Basic programming concepts, types, sequential data structures.
• Programming in Python.
• Searching and sorting algorithms.
• Examples of algorithms, algorithmic strategies.
• Testing and documenting programming code.
• Asymptotic notation and complexity analysis.
• Analyzing program code for correctness, efficiency, and errors.
• Automata theory and formal languages. Turing machine.
• Classes P and NP.

The knowledge and skills to implement and solve algorithmic problems using the mentioned algorithms are developed using Python.

The Numerical Analysis Algorithms course is taught in the second year and is covered in the third semester. Successful completion awards students with five credits, according to ECTS. The primary objective of the course is to develop basic understanding of numerical algorithms, as well as the skills to implement algorithms to solve computer-based mathematical problems. The course covers the following topics:

• Basic numerics, floating-point representation, convergence.
• Horner’s scheme.
• The theory of interpolation: Lagrange polynomial, Hermite interpolation, Neville’s iterative formula.
• Least square approximation.
• Numerical integration: Newton-Cotes formulas, Gaussian quadrature.
• Direct methods for solving systems of linear equations: Gaussian elimination, LU factorization, Cholesky decomposition.
• Householder method.
• Solving nonlinear equations and systems of nonlinear equations: Bisection method, fixed-point iteration, Newton’s method.
• Runge-Kutta methods for ordinary differential equations.
• Characteristic polynomial and eigenvalues.

The knowledge and skills to implement and solve algorithmic problems using the mentioned algorithms were developed using C++ due to its object-oriented programming with high performance, efficient memory management, low-level access to hardware and a rich standard library, including mathematical functions commonly used in numerical algorithms. These allow students to write efficient and customizable numerical algorithms. Objective C++ was one of the courses of the first year of studies.

3.3. The study participants

Study participants were selected from Computer Science students who were enrolled in the two mentioned compulsory courses: Introduction to Computer Science (ICS) (first semester) and Numerical Analysis Algorithms (NAA) (third semester). The first group of students began their studies in the academic year 2019/2020 in a traditional in-person course delivery format that was interrupted because of the confinement. They then continued their studies utilizing the synchronous online format. The second group consisted of students who began their studies in academic year 2020/2021 in the synchronous online format and continued these activities in a hybrid format. The third group of students began their studies in academic year 2021/2022 in a hybrid format that returned to an in-person format in the year 2022/2023. Online learning was supported by Moodle and MS Teams.

Only students enrolled in either the ICS and NAA courses participated in our research. Students who interrupted their studies and did not complete the courses were excluded. Thus, the study group included students who were enrolled in both courses and took both final exams. A total of 787 participants were selected. Table 1 summarizes the study participant groups according to education strategy.

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https://doi.org/10.1371/journal.pone.0305763.t001

Males constituted 87.5% of the total study participants, while females constituted 12.5%. Regarding nationality, the majority, i.e. 85.5%, came from Poland, while 14.5% came from other countries, mainly Ukraine.

3.4. Online exam quizzes

In this study, the Moodle platform provided by the Computer Science Department from the Lublin University of Technology was applied to conduct the final exam process. Comparative analysis of student academic performance was anchored on the results obtained in their final exams. Final exams were carried through the Moodle platform using Quiz activity . All exams comprised questions of various types, including Multiple Choice , Short Answer , Numerical and Essay as follows:

• Multiple choice questions were employed for evaluating both theoretical and practical contents. For our purpose, the option Multiple answers are allowed was used. Multiple answers questions enable one or more answers to be chosen by providing check boxes next to the answers. We used a negative grade percentage for wrong answers, so that simply ticking all choices did not necessarily generate a full grade. If the sum of partial grades was negative, then the total grade for this question would be zero [ 55 ].
• Short answer or numerical questions were used to evaluate theoretical and practical contents. In a short answer question, the student types in a word or phrase in response to a question. This must exactly match one of the acceptable answers. Numerical questions resembled short-answer questions. Here, the difference was that numerical answers were allowed to have an accepted error for number.
• Essay questions were used to evaluate practical contents, mainly programming and coding skills. We employed essay-type questions to provide the option of answering by entering text online. The option Require the student to enter text was chosen. The Response format option was set to Plain text , monospaced font to improve the readability of code by ensuring consistent and clear alignment. This is particularly helpful for maintaining an organized layout. The essay questions had to be marked manually by the course instructor.

The number of multiple choice questions and short answer / numerical questions was comparable. One question was an essay question. Questions were created and stored separately in a Question bank and were organized into 10 categories according to the implemented curricula and learning outcomes. Each category consisted of at least 50 questions. Quiz settings were as follows:

• Quizzes included 20 questions worth 20 points. There were two categories of questions: theoretical and practical.
• Students were allowed to have one attempt at each quiz. The time limit option was set to 60 minutes.
• Students were not allowed to open other windows or programs while taking these quizzes.
• A password was required. The option Block concurrent connections was checked.
• The Choose Sequential navigation method was employed to compel the student to progress through the questions in order and not return to a previous question or skip to a later one.
• The timeframe when the students were able to see feedback was set to the option After the quiz is closed and the option Whether correct was checked.
• Employed questions were assessed for quality and modified for re-use in the next academic year.

Students were tested using the same evaluation methods and types of questions in in-person, synchronous online and hybrid groups. The Moodle platform collected assessment data and generated report statistics. The data containing students’ exam results (points) were collected and exported from the Moodle platform as.xlsx files.

3.5. Quiz report statistics

Quiz statistics provided test statistics and quiz structure analysis. The test statistics gave information on how students performed on a quiz, and employed descriptive statistics: average grade, median grade, standard deviation of grades, skewness and kurtosis. A detailed analysis of each question was given in quiz structure analysis, and applied the following measures: facility index, discrimination index and discriminative efficiency. Discriminative efficiency is a measure similar to discrimination index [ 55 ].

Facility index.

In this work, facility index of a question was determined by the average score divided by the maximum score and represented as a percentage. A higher value indicated an easier question. The interpretation of its values is given in Table 2 [ 55 ].

https://doi.org/10.1371/journal.pone.0305763.t002

Discrimination index.

Discrimination index is the correlation between the score for this question and the score for the whole quiz represented as a percentage. If the score for the question and the score for the test are well correlated, the question can be categorized as a question with good discrimination. The maximum discrimination requires a facility index in the range 30%–70%, although this is not tantamount to high discrimination index. Discrimination index values should be interpreted according to Table 3 [ 55 ].

https://doi.org/10.1371/journal.pone.0305763.t003

A negative value of a discrimination index would mean that the best students got this question wrong more often than the worst students. A discrimination index of zero would mean it was a poor discriminator between good and bad students. Discrimination index is considered excellent when the value is higher than 40%, and considered good when it ranges from 20% to 40%.

Discriminative efficiency.

The discriminative efficiency estimates how good the discrimination index is relative to the difficulty of the question. This attempts to discriminate between students of different ability, and the higher the value, the better is the question at discriminating between students of different abilities [ 55 ]. Values between 30%–50% provide adequate discrimination, while those above 50% provide very good discrimination.

3.6. Statistical analysis

Data collected was tabulated, and analysis was carried out by applying simple percentage analysis, as well as descriptive analysis, using mean, standard deviation and inferential analysis such as t-Student tests and ANOVA [ 56 , 57 ]. We performed non-parametric alternatives such as a Mann-Whitney U test and the Kruskal-Wallis test to compare samples that cannot be assumed to be normally distributed [ 58 , 59 ]. Statistical significance was set at p<0.05. Data analysis was performed using the Statistica Package, Version 13 (TIBCO Software Inc.).

Participants’ profile

Our study included 787 Computer Science students, aged 18 to 22 years. The participant background characteristics revealed that most students were male (87.5%) and native (Polish; 85.5%). Furthermore, most of the students were enrolled in full-time studies (85.5%) ( Table 4 ).

https://doi.org/10.1371/journal.pone.0305763.t004

The percentages of the students who began their studies in the academic years 2019/2020, 2020/2021 and 2021/2022 were comparable, around 30%. An important aspect of the analysis was the availability of data from the pre-pandemic period that was relevant for our investigations.

Comparison of in-person, synchronous online and hybrid learning

The comparison of in-person, synchronous online, and hybrid teaching methods in student learning outcomes based on background characteristics is presented in Tables 5 and 6 .

https://doi.org/10.1371/journal.pone.0305763.t005

https://doi.org/10.1371/journal.pone.0305763.t006

The findings indicated that for the first semester course Introduction to Computer Science, the relation between learning outcomes and student gender was insignificant (p = 0.427). Moreover, the relation between learning outcomes and study option was also insignificant (p = 0.223). However, there was statistically significant difference between learning outcomes and residency status (p < 0.001). The findings indicated that during in-person and online studies, native students had significantly higher learning outcomes than did non-native students (p < 0.001). In addition, full-time students of online studies had significantly higher learning outcomes (p = 0.002) than did part-time students.

Regarding the learning outcomes of the students as obtained in the third semester course Numerical Analysis Algorithms, gender and study options were also insignificant (p = 0.834; p = 0.157) in relation to learning outcomes. In contrast, residency status was significant (p < 0.001). The findings indicate that native students had significantly higher learning outcomes than did non-native students (p < 0.001). Moreover, full-time students of online studies had significantly higher learning outcomes as compared to part-time students (p = 0.011).

The comparison of teaching methods in participant performance based on different semesters (courses) is presented in Table 7 .

https://doi.org/10.1371/journal.pone.0305763.t007

The differences in mean scores related to the first semester course Introduction to Computer Science, during online and hybrid studies, were significantly higher compared to in-person studies (LSD post-hoc, p < 0.001). However, mean scores related to the third semester course Numerical Analysis Algorithms, during online and hybrid studies, were significantly lower in comparison to in-person studies (LSD post-hoc, p < 0.001). Switching to traditional in-person studies in the academic year 2022/2023 did not degrade student performance.

Quiz quality assessment

Tables 8 and 9 reveal the facility index, discrimination index and discriminative efficiency values from the final exams held from 2019/2020 to 2022/2023.

https://doi.org/10.1371/journal.pone.0305763.t008

https://doi.org/10.1371/journal.pone.0305763.t009

The lowest mean facility index was 47% ± 25%, while the highest mean facility index was 59% ± 20%. Moreover, the mean discrimination index was located within the range between 31% and 37% and the mean discriminative efficiency was found within the range between 43% and 54%. The results indicate, with regard to facility index, that most of the questions were moderately difficult, yet about right for the average student, and demonstrated adequate discrimination—regardless of the course delivery format.

5. Discussion and conclusions

In our study, we compared the learning outcomes of Computer Science students who were taught through synchronous online and hybrid systems, to those who learned in the traditional in-person system, and this revealed significantly higher learning outcomes when taught through online and hybrid systems versus in-person. It is worth noting that student scores showed an increasing trend in the years 2019–2023. Despite this, the significant difference in the results of the students’ final examination was not too large–as it did not exceed 10% of the maximal score.

A comparison between the student groups demonstrates that utilizing synchronous online learning can result in more enhanced educational opportunities for students. However, our findings indicated that native students had significantly higher learning outcomes than did non-native students. The reason could be that the study courses were held in Polish, which is a difficult language for non-native students to learn and utilize.

Several research studies have shown that online learning and the combination of online and in-person learning systems have positive and powerful roles in enhancing the effectiveness of education [ 19 , 29 , 41 , 47 , 60 ]. However, along with enhanced accessibility and flexibility, pure online learning also has several disadvantages, notably, the lack of interpersonal contacts and student satisfaction. In the hybrid form, however, flexibility and accessibility are enhanced, while human connection occurs.

Our results indicated that synchronous online learning could be appreciated as a successful method of conducting Computer Science education and can be used as a tool supporting traditional in-person methods. Although this approach is a little less flexible for teachers and students, and requires reliable technology, in comparison to asynchronous learning, this allows for more real time engagement and feedback [ 61 ].

As the effective measurement of knowledge acquired is an important component of Computer Science education, the use of the Moodle quizzes activity as a continuous assessment of students was analyzed according to statistical data such as the facility index, discrimination index and discriminative efficiency. Out of the exam tests conducted from the academic year 2019/2020 to 2022/2023, the mean facility index scores ranged from 47% to 59% and the mean discrimination index ranged from 31% to 37%. The statistic results indicated that, regarding facility index, most of the questions were moderately difficult and about right for the average student regardless of the course delivery format, and that a consistent and adequate level of discrimination indices was maintained. In addition, the similar results obtained in our study no matter the year, with three different groups of students, also confirmed the validity and reliability of the designed exam tests.

Although online learning requires extensive self-discipline, it allows universities to integrate new technologies into their offer, and hence, effectively facilitate the student learning process. After the COVID-19 pandemic, there has been a quick transition back to in-person teaching, but still there are many proffered activities being in an online format. At present, many students state that they prefer to learn through hybrid learning methods. Furthermore, several studies have shown that e-learning methods are used widely by students outside of their formal curricula for continuing their professional education [ 62 ]. This indicates that students and professionals appreciate and take advantage of self-paced learning environments in which they control their learning pace, information flow, selection of learning activities, as well as their time management. Thus, the digital transformation of the educational process has become a necessity to meet shifting student demands and seems to be one of the leading factors that affect current teaching methodology.

It is worth noting that the extreme situation caused by the COVID-19 pandemic provided an opportunity to revise our approach, both to traditional and online learning, but also posed challenges for the future of education systems. In conclusion, the results of the analysis allow us to answer the questions formulated before in the following way.

• The COVID-19 confinement caused online education, which previously was mainly used as an addition to traditional learning methods, to become the mainstream, in particular, in Computer Science.
• The COVID-19 pandemic did not have a disruptive effect that resulted in knowledge deficiency with regard to the academic performance of Computer Science students. In contrast, this situation increased student academic motivation. Indeed, students demonstrated higher exam scores during subsequent two academic years.
• Despite the change from in-person to online learning, the reliability of student assessment remained at similar levels.

6. Limitations and future works

Our context is algorithms and programming in the first two years of the engineering studies program. While we believe that the long period under study is an advantage of this work, its limitation is the fact that it focuses only on the students of Computer Science. We based our research on the data comprising the performance of students in only two courses. Moreover, only the exam scores from the 1 st and 3 rd semesters were included in the study. The courses of other semesters were not assessed because they did not meet the required assumptions regarding the course selection. Another limitation of our study was that students could share information about the content of the exam. However, we randomly assigned students to subcategory sets to avoid sharing information. In the future it is worth considering extending the analysis to students of other fields, as well as take into account student performance in more courses.

Acknowledgments

The authors thank Mr Jack Dunster for linguistic improvement of the text.

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• Education Theory for Online Learning Words: 590
• Information Technology Enabled Online Learning Words: 1099
• Pros and Cons of Online Learning Words: 1102
• Online Learning vs. In-Person Learning Words: 580
• Online Learning Technologies Words: 842
• Theories, Tools, and Principles of Online Learning Words: 832
• Why More and More Students Are Taking Online Classes? Words: 588
• Online Learning and Education Course Reflections Words: 559
• Distance Learning: Advantages and Disadvantages Words: 2754
• Benefits, Challenges, and Opportunities of Distance Learning Words: 2787

Online Learning During the Pandemic

Today’s rapid shift in the traditional patterns of social lifestyle caused by the COVID-19 pandemic outbreak has resulted in the necessity to define possible approaches to living a full-scale life while respecting the need for social distancing. Thus, one of the major challenges in the context was to define the patterns of work and education process during the global lockdown. When it comes to the notion of education, the process of online learning has become a salvation to the problem of education access and efficiency. The definition of online learning stands for an umbrella term that encompasses a series of machine-learning techniques that allow learners to acquire relevant knowledge with the help of technology in a certain sequence [1]. Although the process of online learning has become widely popular due to an ongoing emergency, the term genesis can be traced back to decades prior to COVID-19, as machine learning is also regarded as a scientific outbreak besides being an urgent problem solution [2]. Thus, once the necessity of technological intervention in education became an absolute necessity, there had already been a variety of devices and software applications to implement.

Over the times of the pandemic, the concept of educational technology (EdTech) has become widely popular with software developers and investors. In fact, EdTech, despite a relatively long existence in the market, has now introduced a variety of software applications like Classplus and Edmingle that would facilitate the process of education in both developing and developed countries [3]. Moreover, the already existing educational sources powered by Microsoft and Google are also of great efficiency for today’s learners, as their plain yet efficient design helps students accommodate quickly to the process. Hence, taking everything into consideration, it might be concluded that the process for online education that was rapidly facilitated by a pandemic outbreak is likely to develop greatly over the next few years, creating a full-scale competition for conventional patterns of learning.

S. C. H. Hoi, D. Sahoo, J. Lu, and P. Zhao. “Online learning: A comprehensive survey,” SMU Technical Report , vol. 1, pp. 1-100, 2018.

A. Muhammad, and K. Anwar. “Online learning amid the COVID-19 pandemic: Students’ perspectives.” Online Submission , vol. 2, no. 1, pp. 45-51, 2020.

D. Shivangi. “Online learning: A panacea in the time of COVID-19 crisis.” Journal of Educational Technology Systems , vol. 49, no.1, pp. 5-22, 2020.

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Is Online Learning Effective?

A new report found that the heavy dependence on technology during the pandemic caused “staggering” education inequality. What was your experience?

By Natalie Proulx

During the coronavirus pandemic, many schools moved classes online. Was your school one of them? If so, what was it like to attend school online? Did you enjoy it? Did it work for you?

In “ Dependence on Tech Caused ‘Staggering’ Education Inequality, U.N. Agency Says ,” Natasha Singer writes:

In early 2020, as the coronavirus spread, schools around the world abruptly halted in-person education. To many governments and parents, moving classes online seemed the obvious stopgap solution. In the United States, school districts scrambled to secure digital devices for students. Almost overnight, videoconferencing software like Zoom became the main platform teachers used to deliver real-time instruction to students at home. Now a report from UNESCO , the United Nations’ educational and cultural organization, says that overreliance on remote learning technology during the pandemic led to “staggering” education inequality around the world. It was, according to a 655-page report that UNESCO released on Wednesday, a worldwide “ed-tech tragedy.” The report, from UNESCO’s Future of Education division, is likely to add fuel to the debate over how governments and local school districts handled pandemic restrictions, and whether it would have been better for some countries to reopen schools for in-person instruction sooner. The UNESCO researchers argued in the report that “unprecedented” dependence on technology — intended to ensure that children could continue their schooling — worsened disparities and learning loss for hundreds of millions of students around the world, including in Kenya, Brazil, Britain and the United States. The promotion of remote online learning as the primary solution for pandemic schooling also hindered public discussion of more equitable, lower-tech alternatives, such as regularly providing schoolwork packets for every student, delivering school lessons by radio or television — and reopening schools sooner for in-person classes, the researchers said. “Available evidence strongly indicates that the bright spots of the ed-tech experiences during the pandemic, while important and deserving of attention, were vastly eclipsed by failure,” the UNESCO report said. The UNESCO researchers recommended that education officials prioritize in-person instruction with teachers, not online platforms, as the primary driver of student learning. And they encouraged schools to ensure that emerging technologies like A.I. chatbots concretely benefited students before introducing them for educational use. Education and industry experts welcomed the report, saying more research on the effects of pandemic learning was needed. “The report’s conclusion — that societies must be vigilant about the ways digital tools are reshaping education — is incredibly important,” said Paul Lekas, the head of global public policy for the Software & Information Industry Association, a group whose members include Amazon, Apple and Google. “There are lots of lessons that can be learned from how digital education occurred during the pandemic and ways in which to lessen the digital divide. ” Jean-Claude Brizard, the chief executive of Digital Promise, a nonprofit education group that has received funding from Google, HP and Verizon, acknowledged that “technology is not a cure-all.” But he also said that while school systems were largely unprepared for the pandemic, online education tools helped foster “more individualized, enhanced learning experiences as schools shifted to virtual classrooms.” ​Education International, an umbrella organization for about 380 teachers’ unions and 32 million teachers worldwide, said the UNESCO report underlined the importance of in-person, face-to-face teaching. “The report tells us definitively what we already know to be true, a place called school matters,” said Haldis Holst, the group’s deputy general secretary. “Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.”

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Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

Jessie s. barrot.

College of Education, Arts and Sciences, National University, Manila, Philippines

Ian I. Llenares

Leo s. del rosario, associated data.

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

Literature review

Education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x ̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table ​ Table1 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Participants’ Online Learning Platforms

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

The extent of students’ online learning challenges

Table ​ Table2 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x ̅  = 2.62, SD  = 1.03) with scores ranging from x ̅  = 1.72 ( to some extent ) to x ̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x ̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x ̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x ̅  = 2.51, SD  = 1.31), SIC ( x ̅  = 2.77, SD  = 1.34), and LRC ( x ̅  = 2.93, SD  = 1.31).

The Extent of Students’ Challenges during the Interim Online Learning

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table ​ Table3, 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

Summary of students’ responses on the impact of COVID-19 on their online learning experience

Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table ​ Table4 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table ​ Table4 4 further reveals that strategies used by students within a specific type of challenge vary.

Students’ Strategies to Overcome Online Learning Challenges

Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Authors’ contributions

Jessie Barrot led the planning, prepared the instrument, wrote the report, and processed and analyzed data. Ian Llenares participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing. Leo del Rosario participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing.

No funding was received in the conduct of this study.

Availability of data and materials

Declarations.

The study has undergone appropriate ethics protocol.

Informed consent was sought from the participants.

Authors consented the publication. Participants consented to publication as long as confidentiality is observed.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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What did distance learning accomplish?

Millions of U.S. school children ended their academic year via remote learning. How did this unplanned experiment measure up?

Vol. 51, No. 6 Print version: page 54

• Schools and Classrooms

More than 56 million students attend public and private elementary, middle and high schools in the United States. Last March, the vast majority of them took part in an impromptu experiment when most schools locked their doors to protect against the novel coronavirus. Overnight, teachers were forced to figure out how to translate face-to-face lessons into remote-learning lesson plans.

As schools kick off the 2020–21 school year, there are many unknowns. Some form of distance learning remains likely—either entirely remote, in combination with scaled-back in-person instruction or as a future possibility if new waves of COVID-19 outbreaks emerge.

As educators and administrators plan for that uncertain future, they must also assess how students fared. The pandemic has presented many new challenges in addition to school closures, including the death of loved ones and economic hardship. “Students have been exposed to a tremendous range of experiences, ranging from traumatic to enriched,” says educational psychologist Sara Rimm-Kaufman, PhD, a professor of education at the University of Virginia.

While some students have thrived and learned during the pandemic, others are likely to have fallen behind. Regardless of ZIP code or family background, schools are, in theory, places where all students can receive education and support. But the coronavirus shutdown has emphasized (and widened) existing disparities in education.

“When kids come to a classroom, it’s easy to imagine they’re all the same. But we can’t expect the same outcomes from a kid learning on his own computer at his family’s vacation home and a child who doesn’t even have a table to sit at,” says Avi Kaplan, PhD, a professor of educational psychology at Temple University.

But the experience may yet have a silver lining, he adds. “We have a tendency to go back to what we thought was normal. But there’s an opportunity here to unlearn things that people knew were not working.”

The digital divide

When schools closed abruptly, teachers were forced to design remote-learning plans quickly. The plans they created were all over the map, says Helenrose Fives, PhD, a professor of educational foundations at Montclair State University and president of APA’s Div. 15 (Educational Psychology). In late March, Fives and colleagues began surveying teachers about their experiences with distance learning in New Jersey—a state with a staggering 584 school districts.

“It seems like every district is doing something different. The variability in how districts are approaching this is shocking,” she says.

Even within a single district, student experiences are wide-ranging. Teachers and parents have reported that some kids are thriving with fewer social distractions, or have been energized by their newfound independence. Yet many other children lack devices or reliable access to the internet. And while some families have parents who can oversee their children’s remote learning, many youths are caring for younger siblings while their parents work in essential jobs or living with the chaos of unemployment or homelessness.

“It’s a question of privilege,” says Michele Gregoire Gill, PhD, a professor of educational psychology at the University of Central Florida. “Some families are just in survival mode.”

The inequities are hard to overstate, Gill and other experts say. A survey of 1,500 U.S. families by advocacy group ParentsTogether released in late May found 83% of children in families in the highest income quartile were logging in to distance learning every day. Just 3.7% of those families reported their children were participating in distance learning once a week or less, compared with 38% of students from families in the lowest income quartile.

That missed instructional time is likely to be a serious setback for low-income students. Previous research has found that chronic absenteeism—usually defined as missing at least 10% of school days—affects reading levels, grade retention, graduation rates and dropout rates (Allison, M.A., et al., Pediatrics , Vol. 143, No. 2, 2019). Chronic absenteeism disproportionately affects kids living in poverty in the best of times, as Children’s National Hospital pediatrician Danielle Dooley, MD, and colleagues describe in an opinion piece on the effects of COVID-19 on low-income children ( JAMA Pediatrics , published online, 2020). Remote learning during COVID-19 is likely to widen that disparity, they say.

Students from low-income homes aren’t the only ones at risk of slipping through the cracks. Families who speak other languages, undocumented immigrants and students with special needs are also at risk of missing out on the services to which they’re entitled. Children with disabilities or special needs are legally entitled to special education services, including speech-language therapy, autism interventions, occupational therapy and psychological services. But many of those don’t translate easily to the remote platforms available. The ParentsTogether survey painted a grim picture for special education students, with 40% of parents reporting they weren’t receiving any support, and just 20% reporting their children were receiving all of the special education services they typically received in school.

Does remote learning work?

Students from disadvantaged backgrounds and those with special needs may face the biggest educational challenges. But some research indicates that all students could start the year far behind. Megan Kuhfeld, PhD, and Beth Tarasawa, PhD, of the Collaborative for Student Growth at the educational nonprofit organization NWEA, published a white paper analyzing past research on learning loss over summer break. They predict that overall, students in grades three through eight will return to school with roughly 70% of the learning gains in reading and less than 50% of the learning gains in math compared with a typical year ( The COVID-19 Slide: What Summer Learning Loss Can Tell Us About the Potential Impact of School Closures on Student Academic Achievement , Collaborative for Student Growth, 2020).

That’s not to say online learning itself isn’t effective. “Research generally shows that online learning can be as effective as in-person instruction, if you have a good setup,” Gill says. But what most schools were doing in the spring wasn’t true online learning, she adds. “Teachers didn’t have prepared online content, so they were trying to convert what they normally do to an online platform. It was emergency triage.”

“Remote learning is not the same as online learning,” agrees Aroutis Foster, PhD, a professor of learning technologies at Drexel University. True online learning happens on digital platforms designed for that purpose, often with personalized content for each student and options to use their choice of digital tools. “Online learning facilitates different types of learning preferences, provides learner flexibility and uses online quality metrics,” Foster says. But for many students, distance learning during COVID-19 included none of those features, and instead involved tuning in at a set time to listen to teachers lecture on Zoom or Google Meet.

What’s more, online learning programs that were working before coronavirus might not be as effective without teacher support and the structure of in-person learning. In a data tool called the Opportunity Insights Economic Tracker , economists at Brown University and Harvard University looked at how U.S. students were performing in an online math program before and after the coronavirus shutdown. As of May 31, total student progress in online math coursework decreased by 64.2% compared with January. In low-income ZIP codes, math progress fell 74.8%, compared with 36.1% in high-income ZIP codes.

Connecting lessons to children’s interests is especially important in remote settings where students don’t have the classroom structure to guide them.

Successful learning environments

With continued remote learning a distinct possibility, educators will be considering what went well during the spring of 2020, and what they can improve on. Educational psychology offers clues about what factors are important to creating successful learning environments. To stay motivated when learning at home, students need to feel competence, relatedness (a sense of belonging and connection with others) and autonomy, says Kaplan. According to self-determination theory (Ryan, R.M., & Deci, E.L., American Psychologist , Vol. 55, No. 1, 2000), those needs are vital for self-motivation and well-being in many domains, including education. In a practice brief for parents who are homeschooling during quarantine ( Homeschooling Under Quarantine , APA Div. 15, 2020), Kaplan and Debra A. Bell, PhD, describe how parents can support a child’s competence (emphasize improvement with realistic expectations), relatedness (consider a child’s needs, listen empathetically and provide emotional support) and autonomy (provide meaningful choices and allow a child to incorporate personal interests).

Tying lessons into children’s own interests may be especially important in remote settings, Foster says, when students don’t have the classroom structure and classmates’ behaviors to guide them. “Online settings require a lot of self-regulation, and we know novice learners don’t have a lot of that,” he says. “Peer influence is a huge deal in terms of learning, and there’s a lot of socially shared regulation happening in classrooms.”

The lack of social connections during the pandemic is significant, says Rimm-Kaufman. “One of the things that this shift has underscored is how much personal relationships matter for kids, including relationships with other students and with teachers.”

Feeling connected to a teacher can make a big difference in educational outcomes. The quality of teacher-student relationships has a significant effect on student engagement and, to a slightly lesser degree, on student achievement, according to a meta-analysis of 99 studies (Roorda, D.L., et al., Review of Educational Research , Vol. 81, No. 4, 2011). The influence of those relationships was particularly important for students from disadvantaged backgrounds and those with learning difficulties.

But meaningful teacher relationships may be harder to develop over the internet, says Fives. “So much of the motivation in a classroom comes from those quick interactions students have with teachers in the moment,” she says. “In a remote-learning setting, kids often have to wait for that feedback.”

What’s more, digital interactions can be highly taxing, Kaplan says. In person, teachers and students learn a lot from the mood of the classroom and subtle body language. In a video, it’s harder to discern those details. “Online, much of that information is missing, so our brains try to fill in the gaps. And that takes working memory,” Kaplan says. “At the same time, students might see their own image, which can raise their self-consciousness and is an added burden while trying to focus on learning.”

Learning new technology has also presented a challenge, Fives adds. “It’s not just writing an essay. It’s figuring out how to post it to the platform, how to log in to get the feedback from the teacher,” she says. Older students might have to learn different platforms for different classes, she adds. “Every teacher might be using different tools, and that puts a heavy cognitive load on students.”

Learning losses and teacher burnout

Given so many hurdles—known and unknown—educators will have to be flexible as the new academic year begins, Foster says. “It will be an atypical year, and there will absolutely be a lot of catching up.”

An important next step will be to figure out how best to assess students’ knowledge as they start the new year, Rimm-Kaufman says. “Some kids will come in having lost months of instruction, so educators will have to make broader assessments than they usually would, and find ways to adjust their instruction accordingly.”

That is a daunting task, though not an insurmountable one, says Francesca López, PhD, an educational psychologist and the Waterbury Chair of Secondary Education at Penn State University’s College of Education. “Teachers do remarkable work, and I don’t believe for a second this generation of students won’t catch up,” she says. “But we can’t allow everything to rest on teachers. Policies must change to ensure equity.”

In the short term, López adds, educators will have to attend to students’ emotional well-being to help them learn. Millions of families have experienced unemployment and financial hardship, and many children have lost loved ones to COVID-19. “This is a traumatic event, and we need to prioritize mental health,” López says. “We can’t focus on academics without considering the whole child.” (See companion article, “ Safeguarding Student Mental Health ”.)

Teacher mental health, too, is a top priority, experts say. At the end of March, Marc Brackett, PhD, founder of the Yale Center for Emotional Intelligence at Yale University, and colleagues surveyed more than 5,000 U.S. teachers, asking them to list the most frequent emotions they felt each day. The top three: anxiety, fear and worry. “We found [educators] are more anxious than ever before, and they’re struggling to manage their anxiety,” Brackett says. “The uncertainty and unpredictability about what the future of school will be is taking a toll on their wellness.”

Teachers aren’t just learning new platforms. They’re also worrying about student well-being more than ever before and having to figure out how to reach out to them from their own homes. Plus, says Rimm-Kaufman, “many schools emphasize teacher collaboration, and those efforts are strained when teachers aren’t in the same building with one another.” It’s unsurprising that many teachers experienced stress, burnout and self-doubt as they taught in such unprecedented circumstances in the spring, Fives adds. “Many really good teachers don’t feel like good teachers anymore. Their identity as a teacher is affected, and their self-efficacy is crashing.”

Investing and innovating

Administrators face an uphill battle as they find ways to support teachers and get students back on track. School budgets are vulnerable to shrinking state revenues due to the pandemic, and some school districts have already laid off employees. In May, school superintendents from 62 cities sent a letter to Congress asking for new federal education assistance. “Significant revenue shortfalls are looming for local school districts that will exacerbate the disruption students have already faced,” the letter warned.

Still, some experts are hopeful that this experience could be the shake-up that schools needed to improve education for all children. Educational disparities will be hard to ignore in the wake of the pandemic, Kaplan says. “Crises often sharpen our gaze and reveal aspects of our lives that were masked or ignored. This highlights the need for prioritizing equity at the policy level.”

“We’re shifting into the unknown,” López says. “Educational psychology has a robust history of learning theories. As this unfolds, we need to look to the research to see what we can learn, and how we can incorporate it into high-quality education.”

Further reading

Improving School Improvement Adelman, H., & Taylor, L., Center for Mental Health in Schools & Student/Learning Supports at UCLA

Low-Income Children and Coronavirus Disease 2019 (COVID-19) in the U.S. Dooley, D.G., et al., JAMA Pediatrics , 2020

School Reopening—The Pandemic Issue That Is Not Getting Its Due Christakis, D.A., JAMA Pediatrics , 2020

Impact of Online Learning in K–12: Effectiveness, Challenges, and Limitations for Online Instruction Ward-Jackson, J., & Yu, C., In Handbook of Research on Blended Learning Pedagogies and Professional Development in Higher Education , IGI Global, 2019

Recommended Reading

Online inequities

How many children weren’t engaging with remote learning (logging in once a week or less)?

• 3.7% of children in families making more than $100,000 per year
• 38% of children in families making less than $25,000 per year

Source: ParentsTogether

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The pandemic has had devastating impacts on learning. What will it take to help students catch up?

Subscribe to the brown center on education policy newsletter, megan kuhfeld , megan kuhfeld senior research scientist - nwea jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea karyn lewis , and karyn lewis director, center for school and student progress - nwea emily morton emily morton research scientist - nwea.

March 3, 2022

As we reach the two-year mark of the initial wave of pandemic-induced school shutdowns, academic normalcy remains out of reach for many students, educators, and parents. In addition to surging COVID-19 cases at the end of 2021, schools have faced severe staff shortages , high rates of absenteeism and quarantines , and rolling school closures . Furthermore, students and educators continue to struggle with mental health challenges , higher rates of violence and misbehavior , and concerns about lost instructional time .

As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students’ academic achievement has been large. We tracked changes in math and reading test scores across the first two years of the pandemic using data from 5.4 million U.S. students in grades 3-8. We focused on test scores from immediately before the pandemic (fall 2019), following the initial onset (fall 2020), and more than one year into pandemic disruptions (fall 2021).

Average fall 2021 math test scores in grades 3-8 were 0.20-0.27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores were 0.09-0.18 SDs lower. This is a sizable drop. For context, the math drops are significantly larger than estimated impacts from other large-scale school disruptions, such as after Hurricane Katrina—math scores dropped 0.17 SDs in one year for New Orleans evacuees .

Even more concerning, test-score gaps between students in low-poverty and high-poverty elementary schools grew by approximately 20% in math (corresponding to 0.20 SDs) and 15% in reading (0.13 SDs), primarily during the 2020-21 school year. Further, achievement tended to drop more between fall 2020 and 2021 than between fall 2019 and 2020 (both overall and differentially by school poverty), indicating that disruptions to learning have continued to negatively impact students well past the initial hits following the spring 2020 school closures.

These numbers are alarming and potentially demoralizing, especially given the heroic efforts of students to learn and educators to teach in incredibly trying times. From our perspective, these test-score drops in no way indicate that these students represent a “ lost generation ” or that we should give up hope. Most of us have never lived through a pandemic, and there is so much we don’t know about students’ capacity for resiliency in these circumstances and what a timeline for recovery will look like. Nor are we suggesting that teachers are somehow at fault given the achievement drops that occurred between 2020 and 2021; rather, educators had difficult jobs before the pandemic, and now are contending with huge new challenges, many outside their control.

Clearly, however, there’s work to do. School districts and states are currently making important decisions about which interventions and strategies to implement to mitigate the learning declines during the last two years. Elementary and Secondary School Emergency Relief (ESSER) investments from the American Rescue Plan provided nearly $200 billion to public schools to spend on COVID-19-related needs. Of that sum, $22 billion is dedicated specifically to addressing learning loss using “evidence-based interventions” focused on the “ disproportionate impact of COVID-19 on underrepresented student subgroups. ” Reviews of district and state spending plans (see Future Ed , EduRecoveryHub , and RAND’s American School District Panel for more details) indicate that districts are spending their ESSER dollars designated for academic recovery on a wide variety of strategies, with summer learning, tutoring, after-school programs, and extended school-day and school-year initiatives rising to the top.

Comparing the negative impacts from learning disruptions to the positive impacts from interventions

To help contextualize the magnitude of the impacts of COVID-19, we situate test-score drops during the pandemic relative to the test-score gains associated with common interventions being employed by districts as part of pandemic recovery efforts. If we assume that such interventions will continue to be as successful in a COVID-19 school environment, can we expect that these strategies will be effective enough to help students catch up? To answer this question, we draw from recent reviews of research on high-dosage tutoring , summer learning programs , reductions in class size , and extending the school day (specifically for literacy instruction) . We report effect sizes for each intervention specific to a grade span and subject wherever possible (e.g., tutoring has been found to have larger effects in elementary math than in reading).

Figure 1 shows the standardized drops in math test scores between students testing in fall 2019 and fall 2021 (separately by elementary and middle school grades) relative to the average effect size of various educational interventions. The average effect size for math tutoring matches or exceeds the average COVID-19 score drop in math. Research on tutoring indicates that it often works best in younger grades, and when provided by a teacher rather than, say, a parent. Further, some of the tutoring programs that produce the biggest effects can be quite intensive (and likely expensive), including having full-time tutors supporting all students (not just those needing remediation) in one-on-one settings during the school day. Meanwhile, the average effect of reducing class size is negative but not significant, with high variability in the impact across different studies. Summer programs in math have been found to be effective (average effect size of .10 SDs), though these programs in isolation likely would not eliminate the COVID-19 test-score drops.

Figure 1: Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) Table 2; summer program results are pulled from Lynch et al (2021) Table 2; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span; Figles et al. and Lynch et al. report an overall effect size across elementary and middle grades. We were unable to find a rigorous study that reported effect sizes for extending the school day/year on math performance. Nictow et al. and Kraft & Falken (2021) also note large variations in tutoring effects depending on the type of tutor, with larger effects for teacher and paraprofessional tutoring programs than for nonprofessional and parent tutoring. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

Figure 2 displays a similar comparison using effect sizes from reading interventions. The average effect of tutoring programs on reading achievement is larger than the effects found for the other interventions, though summer reading programs and class size reduction both produced average effect sizes in the ballpark of the COVID-19 reading score drops.

Figure 2: Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; extended-school-day results are from Figlio et al. (2018) Table 2; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) ; summer program results are pulled from Kim & Quinn (2013) Table 3; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: While Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span, Figlio et al. and Kim & Quinn report an overall effect size across elementary and middle grades. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

There are some limitations of drawing on research conducted prior to the pandemic to understand our ability to address the COVID-19 test-score drops. First, these studies were conducted under conditions that are very different from what schools currently face, and it is an open question whether the effectiveness of these interventions during the pandemic will be as consistent as they were before the pandemic. Second, we have little evidence and guidance about the efficacy of these interventions at the unprecedented scale that they are now being considered. For example, many school districts are expanding summer learning programs, but school districts have struggled to find staff interested in teaching summer school to meet the increased demand. Finally, given the widening test-score gaps between low- and high-poverty schools, it’s uncertain whether these interventions can actually combat the range of new challenges educators are facing in order to narrow these gaps. That is, students could catch up overall, yet the pandemic might still have lasting, negative effects on educational equality in this country.

Given that the current initiatives are unlikely to be implemented consistently across (and sometimes within) districts, timely feedback on the effects of initiatives and any needed adjustments will be crucial to districts’ success. The Road to COVID Recovery project and the National Student Support Accelerator are two such large-scale evaluation studies that aim to produce this type of evidence while providing resources for districts to track and evaluate their own programming. Additionally, a growing number of resources have been produced with recommendations on how to best implement recovery programs, including scaling up tutoring , summer learning programs , and expanded learning time .

Ultimately, there is much work to be done, and the challenges for students, educators, and parents are considerable. But this may be a moment when decades of educational reform, intervention, and research pay off. Relying on what we have learned could show the way forward.

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August 20, 2024

Teachers’ Feedback on Using Discord as an Online Learning Platform

Uong, T. G. T., Nguyen, D. K., & Nguyen, H. N. (2022). Teachers’ Feedback on Using Discord as an Online Learning Platform. International Journal of TESOL & Education, 2(4), 84-104. https://doi.org/10.54855/ijte.22246

21 Pages Posted:

Tri Uong Tran Gia

University of Social Sciences and Humanities – Vietnam National University HCMC

Khoi D. Nguyen

Ho chi minh city open university, nhon h. nguyen.

Date Written: May 20, 2024

The COVID-19 pandemic has called for a shift in the teaching and learning landscape from conventional classes to e-learning. This propels the use of a range of online learning and distance learning platforms massively, notably MS Teams, Zoom US, and Google Classroom. However, the fact that the aforementioned require a monetary subscription to unlock their full potential proves detrimental to the accessibility to education during the pandemic, i.e., not all students and/ or educational institutions have the available means. This paper thus seeks to affirm the capability of Discord as an alternative online learning platform that is not only efficient in its own right but also comes at no expense. To this end, a handful of English teachers who had been teaching online via either of the three platforms above were offered to switch to Discord for a fixed amount of time. They received instructions and support from the research team concerning the platform along the way and were asked to participate in a survey afterward. With the use of SPSS for statistical data analysis, the paper pointed out that Discord achieved a high compatibility level for both parties in use, namely the teachers and the students.

Keywords: e-learning, distance learning, online learning, conferencing software, Discord

Suggested Citation: Suggested Citation

Tri Uong Tran Gia (Contact Author)

University of social sciences and humanities – vietnam national university hcmc ( email ).

Ho Chi Minh Vietnam

Ho Chi Minh City Open University ( email )

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• Open access
• Published: 19 August 2024

Navigating undergraduate medical education: a comparative evaluation of a fully online versus a hybrid model

• Anila Jaleel 1 ,
• Saleem Perwaiz Iqbal 1 ,
• Khalid Mahmood Cheema 1 ,
• Sundus Iftikhar 1 &
• Muhammad Zahid Bashir 1  

BMC Medical Education volume  24 , Article number:  895 ( 2024 ) Cite this article

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Metrics details

The evaluation of undergraduate medical curricula plays a crucial role in ensuring effectiveness and helps in continuous improvement of the learning process. This study aims to compare the effectiveness of online and hybrid teaching models of the first-year MBBS curriculum in the COVID-19 era (2019–20) and the para-COVID-19 pandemic (2020–21).

Study methodology

Mixed methods study with CIPP model was used. Data was collected by administering a survey and focus group discussions (FGDs) with first-year students from the 2019–2020 and 2020–2021 cohorts, faculty and administrators, which were recorded for analysis. Recorded lectures, guidebooks, planners, and question papers were also scrutinized for quality and adequacy. Furthermore, admission merit, module assessments, and professional examination results were compared and correlated. The learning environment was evaluated through the questionnaire (validated and used by Pakistan Medical and Dental council for inspections of medical schools) and the facilities provided in both years were juxtaposed. The study utilized NVIVO for qualitative and SPSS version 23 for quantitative data analysis.

Contextual analysis underscored the critical need for online teaching during the COVID-19 pandemic, with provided resources being deemed sufficient. Notably, the student-faculty ratio stood at 4:1, and essential resources were readily available. The fully online batch outperformed the hybrid teaching class in 2020–21. Process analysis revealed successful session delivery in hybrid and online through webinars and Zoom, accompanied by timely provision of study guides and punctual assessments. Moreover, examination papers demonstrated acceptable reliability (Cronbach’s alpha: 0.61) in core subjects. Product analysis indicated that the 2020–21 cohort performed better in modular and professional examinations across all subjects ( P  < 0.01) despite their lower admission merit compared to the 2019–20 batch.

Conclusions

The study revealed challenges faced during total online teaching, highlighting knowledge and skills gaps in students. While students favored hybrid teaching for interaction, faculty preferred online strategies and suggested blended learning. The administration recognized faculty’s swift transition but stressed the need for blended learning workshops and strengthening the medical education department. Recommendations include implementing blended learning strategies, conducting faculty workshops, equipping the medical education department for online teaching, and gathering student feedback after each module to enhance the curriculum.

Peer Review reports

The outbreak of the COVID-19 pandemic triggered a rapid and unprecedented transformation in the global education landscape. Educational institutions worldwide faced the urgent task of ensuring continued high-quality learning experiences while prioritizing the safety and well-being of students, faculty, and staff [ 1 ]. In March 2020, when the pandemic hit Pakistan, medical schools were compelled for transition to online teaching methods, marking a significant departure from conventional educational delivery modes [ 2 ].

The challenges posed by the pandemic extended beyond the classroom, encompassing broader societal, technological, and pedagogical dimensions. In response, educators and institutions embraced innovation and reimagined traditional instructional methods [ 3 ]. Online and hybrid teaching emerged as practical solutions to sustain learning amidst uncertainty. In Pakistan, principals directed medical education departments to swiftly initiate faculty training for online teaching, ensuring educators were equipped to deliver sessions according to predefined plans within a week. Students experienced a blend of online and on-campus learning including lectures, small group discussions and practical demonstration through video, adjusting to synchronous and asynchronous teaching methods. Despite ongoing research in developed countries, the unique challenges faced in Pakistan, such as limited internet access, smartphone and laptop availability, and connectivity issues, underscored the need for research tailored to developing countries’ contexts [ 2 ].

Recognizing blended learning as a future educational tool post-COVID-19, evaluating its effectiveness became imperative. The study focused on evaluating the first-year integrated MBBS program of the 2019–20 fully online batch versus the 2020–21 hybrid batch in a private medical college in Lahore, employing the CIPP (Context, Input, Process, and Product) Evaluation Model for Educational Accountability” [ 4 ]. This comprehensive model facilitated both internal and external evaluations, ensuring credibility, accountability, and informed decision-making in education [ 3 ].

The study aimed to assess the curriculum’s implementation, course objectives achievement, and provide feedback for future program development or implementation. By comparing the context, input, process, and output of the first-year MBBS curriculum during the COVID-19 era, the study aimed to identify strengths, weaknesses, and areas for improvement, guiding modifications to future curricula. This endeavor reflected a proactive approach to adapting educational practices in response to unprecedented challenges, with a focus on continuous improvement and innovation.

Literature review

Program evaluation is an important tool for evaluating the quality of any educational program. A systematic review on CIPP model was done by Toosi et al., (2021) highlights the importance of this model to evaluate the managerial skills, faculty performance, students’ knowledge, facilities available, financial implications and policies for an educational program [ 5 ]. Another study was conducted at Shiraz medical school, Iran to evaluate the integrated basic sciences curriculum using CIPP model [ 6 ]. The authors concluded that this model significantly help policy makers to make decisions in the right direction. An educational framework was designed to evaluate the WFME accreditation basic standards in medical education. Logic model was used to convert the standards into evaluable items which can be used by medical schools for self-review and also can be adaptable by the accrediting bodies [ 7 ]. A study carried out in India developed competency-based tools to evaluate a community-based teaching program using Delphi technique [ 8 ]. The studies highlight the importance of program evaluation in medical education to evaluate the programs comprehensively and guide the policy makers to make informed decisions.

Significance of the study

This study will help us to identify the preferred method of teaching and learning based on evidence and highlights the gaps in the online versus hybrid methods of teaching.

The study employed a convergent mixed-method cross-sectional investigation where focus group discussions, interviews and documents review were conducted and results were compared and compiled after the completion of qualitative and quantitative analysis. Multiple data sources were used i.e. triangulation was done to fully understand the effectiveness of the program.

Setting and participants

Study was conducted at a Private Medical College established in 2010. The duration of study was one year between May 2022 and June 2023. Employing a decision-oriented CIPP model, the research included 300 MBBS first-year students from the 2019–20 and 2020–21 batches, as well as 50 faculty members who taught these students, along with administrators.

Recruitment

Participants were selected using cluster sampling technique, with students from the 2019–20 batch classified as Group A and those from the 2020–21 batch as Group B. Emails were sent to the students of both years to participate in the study and give informed consent. Faculty who have taught these years as well as principal and administrators ( Director student affairs department and Director medical education department) were also sent invitation via email to give consent and protected time for an interview. In ensuring voluntary participation, this study adopted several key strategies to prevent coercion and uphold ethical standards. Firstly, students and faculty were provided with comprehensive information about the study’s purpose, procedures, and potential risks, enabling them to make an informed decision about participation. This was reinforced by emphasizing that participation was entirely voluntary and that students had the right to withdraw at any stage without penalty. Moreover, confidentiality and anonymity were assured to safeguard privacy and encourage honest responses. Language used in all communication was carefully crafted to avoid coercion, explicitly stating the absence of negative consequences for non-participation. Ethical oversight from an institutional review board ensured adherence to ethical guidelines, with any concerns regarding coercion promptly addressed. Lastly, participants were offered access to support resources and provided with contact information for the research team, fostering an environment where their well-being was prioritized. Through these measures, the study endeavored to promote voluntarism and ethical conduct among participants, maintaining the integrity of the research process.

Individuals who did not provide consent were excluded from the study. No personally identifiable information, such as names, was collected. A committee comprising a member from Medical Education (Co-Investigator, along with the Principal Investigator as evaluators), worked closely with administrators following project approval by the Institutional Review Board of Shalamar Medical and Dental College ( IORG 0010289 IRB No: 0420 Reference No: SMDC-IRB/AL/32/2022) , in accordance with the Helsinki Declaration (6/EA/FKGUI/VI/2022 ).

Data collection

Quantitative data collection.

The committee conducted an evaluation utilizing a questionnaire aligned with the standards set forth by the Pakistan Medical & Dental Council (PMDC), with 158 items in curriculum organization and management section and 42 items in infrastructure section, as outlined in their publication ( https://pmdc.pk/Publication/Standards ).

Qualitative data collection

This evaluation involved inspecting facilities, conducting interviews, and facilitating focus group discussions (FGDs) after obtaining informed consent from the participants. In-depth interviews were carried out using a semi-structured guide, with the questionnaire validated through a pilot study involving 10–15 MBBS students. Each participant was allotted 30 min for participation in either focus group discussions (FGDs) or interviews, scheduled based on their availability. Multiple researchers (AJ, ZB, SP, and KMC) conducted the interviews with participants, ensuring audio recordings and written documentation to minimize bias. Non-verbal cues were also observed during the study. Interviews were conducted in both English and Urdu, later translated and transcribed accordingly. A total of 10 interviews were conducted, with researchers determining saturation had been achieved. The FGD was conducted in a confidential conference room setting. Committee members reviewed data from relevant departments and medical education concerning the first year, with all data stored on password-protected computers for confidentiality.

To assess the context , surveys, and interviews were conducted, focusing on PMDC standards. For input evaluation, observations were made regarding the available human and material resources based on PMDC inspection criteria. This included reviewing documents, administering feedback questionnaires to faculty and students, and conducting pilot attempts. The process evaluation involved conducting FGDs with faculty, students, and administrators. Additionally, observations were made of recorded lectures from online classes, descriptions of the actual teaching process, continuous interaction with program operation faculty and staff, and observation of their activities. For product evaluation, data on performance in module and professional examinations were collected. This comprehensive approach allowed for a thorough assessment of the curriculum and its outcomes.

Data analysis

Quantitative data was analyzed using SPSS ver 23. For quantitative variables, Crohnbach’s alpha was used to determine reliability. Mean and standard deviation (SD) were computed, with an independent samples t-test employed to compare groups. A significance level of P  < 0.05 was deemed statistically significant, guiding the interpretation of findings. Qualitative data was analyzed using NVIVO. Phenomenological framework was followed to identify themes, coding themes and subthemes. Data coding was undertaken to identify themes, with coding, themes, and subthemes agreed upon by all researchers to mitigate bias. Themes were organized according to interview questions. Nodes and sub-nodes were established to organize qualitative data, facilitating the identification of themes and sub-themes. Qualitative data collection continued until saturation was attained, ensuring comprehensive coverage of relevant insights. Coded data was reviewed and discussed by the study team to avoid any duplication and consensus was reached.

Data integration

Qualitative and quantitative data was aligned by analyzing the detailed findings along with the results of questionnaire. The study team analyzed the areas of convergence and divergence and comprehend the expansion of findings in questionnaire to detailed discussions in focus groups and interviews.

Qualitative analysis involved conducting focus group discussions (FGDs) with 10 groups, each comprising 15 students. The resulting themes were as follows:

Perception of the usefulness of study guides : Group A students expressed mixed opinions, with 75% finding the study guides helpful and 25% considering them not useful. Conversely, Group B found them helpful overall, but some students suggested a need for better emphasis on how to effectively utilize them (Fig.  1 a). Students quotations are shown in Table  1 .

Word cloud. a. Most frequently used word was yes study guides effective followed by faculty, students, teaching online, study useful, PBL and assessment. b. Items clustered by word similarity (First year MBBS students). c. Attendance and Assessment Online was coded most frequently followed by SGS and PBL and advantages and disadvantages of on campus and online teaching. Least frequently coded were faculty, challenges and affective domain

Figure  2 : Feedback from Students Groups A and B .

Utilization of study guidebooks : Group A students utilized study guides for tasks such as making short notes, summarizing studies comprehensively, revising, and determining what to study. However, some students initially encountered difficulties in using them effectively, as noted by Group B, and only managed to overcome these challenges after completing two modules. Direct quotes from students are shown in Table  1 .

Benefits of study guidebooks : In Group A, students found study guides beneficial for enhancing knowledge, covering the syllabus comprehensively, highlighting important topics, and filtering out significant content. Additionally, they valued the learning objectives and slides provided by the teachers. Conversely, Group B students found study guides helpful for defining what needs to be studied, filtering out important topics, and guiding them on a clear path (Fig.  2 ). Students remarks are shown in Table  1 .

Feedback from Students Groups A and B. a. Qualitative responses of FGD batch 2019–2020: First year MBBS students (Group A): These students discussed in detail the differences of SGS and PBL online followed by discussion on online teaching the most. b. Qualitative responses of FGD batch 2020-21: First year MBBS students (Group B): The highest response of the students were related to advantages and disadvantages of online teaching/learning followed by implementation of learning strategies online

Perception of learning outcomes : Mixed opinions were gathered from students in Group A, with some acknowledging the study guides as well-defined and comprehensive in covering every topic, while others did not share this view. Conversely, students in Group B found the learning outcomes to be well-defined and inclusive of every topic (Fig.  1 ). Students views are depicted in Table  1 .

Implementation of learning outcomes : Group A students found that learning outcomes were not truly implemented and improvements were needed, such as smaller group sessions or greater understanding of teachers regarding their significance. Group B students generally provided positive comments, stating that most of the content was covered. No specific areas of improvement were mentioned. Students comments are shown in Table  1 .

Effectiveness of teaching sessions : Students generally found the teaching sessions effective and aligned with the learning outcomes, with some students suggesting the use of more multimedia and a wider spectrum of topics. However, group B students were concerned about the coordination between faculty members and found that the teaching sessions did not correspond with the learning outcomes (Fig.  1 ). Students remarks are shown in Table  1 .

Usefulness of SGDs and PBL : Students generally found SGDs (Small Group Discussions) and PBL (Problem-Based Learning) useful for clinically oriented knowledge, improving skills, and increasing confidence (Fig.  1 ). However, some students found them somewhat helpful and suggested improvements such as providing topics earlier and covering a wider spectrum of topics (Fig.  2 a). Group B students found SGDs and PBL useful for creating long-term memory, creating interest, and offering different perspectives. Some students suggested a need for more tutorials. Students’ perceptions are shown in Table  1 .

Effectiveness of practical sessions : Most students in Group A found practical sessions useful for improving skills, but some students found them unnecessarily long and suggested lessening the time (Fig.  1 ). Group B students found practical sessions useful, but some students suggested allowing everyone to get the opportunity to use instruments by themselves. Table  1 shows students remarks about it.

Assessment of the affective domain : Most students found that the affective domain was mentioned in guidebooks and assessed by the faculty members. Group B students observed that the affective domain was mentioned in guidebooks but not assessed, with some students suggesting the use of log books and PBL forms for it.

Comparison of online teaching during COVID and in-class teaching during the non-COVID era : Students generally found in-class teaching more effective and interactive, but they appreciated that teachers provided them with slides of presentations for online teaching. Group B students found Zoom sessions useful but not webinars for online teaching during COVID.

Advantages and Disadvantages of Online Teaching : Recorded lectures were the most significant advantage of online teaching, as they can be accessed from home comfort and can be played again if needed. However, the lack of interaction between students and teachers and the presence of many distractions were major disadvantages. The perception of group B students was that home comfort and no need to travel were the main advantages of online teaching, while network issues and a lack of practical experience were the most significant disadvantages (Fig.  3 b). Table  1 shows students remarks about it. ”

Advantages and Disadvantages of On-Campus Teaching : Group A students found that on-campus teaching was beneficial in terms of one-to-one interaction with teachers, more interaction with peers, and hands-on experience. However, long hours, lengthy lectures, and being time-consuming were the main drawbacks. Group B students cited punctuality, routine, and interaction with teachers as advantages. In contrast, time taken for transportation and variable teacher quality were disadvantages. For students’ perceptions see Table  1 .

Qualitative analysis of responses from administration and faculty

The interviews conducted with the administration, which includes the principal and the Director of Student Affairs (DSA), and the faculty exposed a number of themes related to the experience of online teaching during COVID-19.

Satisfaction with Online Teaching : Participants had mixed feelings regarding the usefulness and satisfaction with online teaching. The principal considered it a contextual and useful option, whereas the director of student affairs (DSA) found it ineffective due to a lack of interest and two-way communication. The DSA was of the view that forced compulsion to attend was not useful since two-way communication between teachers and students was lacking. The faculty maintained that they had initial problems related to technical aspects, but they learned to tackle these issues in a few weeks. However, the faculty had serious concerns related to the practicals, as they could not be conducted in an online setting, hence the practical application of knowledge suffered a great deal. This concern was particularly raised by the anatomy department, as the faculty felt that gross anatomy could not be taught properly. The students, however, preferred online lectures as they did not have to travel or commute, so they could concentrate more on their studies. Challenges Faced During the COVID-19 era, the administrators and faculty faced various challenges in teaching and assessment. The principal expressed concerns regarding the inability of senior faculty members to operate online modalities and utilize them appropriately. All the respondents unanimously agreed that network issues were a major hindrance to conducting online classes smoothly (Fig.  3 ). The faculty also stated that proctoring during assessments was ineffective and students could easily cheat; with identical answers being observed in SEQs. According to the faculty, they were only able to demonstrate the skill, but they lacked a means of determining whether or not the students had actually acquired the skill. Similarly, problem-based learning (PBL) sessions could not be conducted, leading to unsatisfactory results. The DSA noticed that even after the lockdown had lifted, fewer students were attending the classes. They blamed the lack of engagement during online lectures for this fallout in face-to-face lecture attendance, as students were finding it hard to return to the engaging routine of lectures post-COVID.

Faculty Training and Performance : The participants had mixed views regarding the faculty’s training and performance. The principal suggested that reverse mentoring might help in troubleshooting technology issues with senior faculty. He was of the view that, “Junior faculty is more tech savvy , so reverse mentoring helped a lot during COVID-19.” The DSA considered the faculty quick in catching up with technology, and the faculty indicated that the Medical Education department trained them well in time. He claimed that the “The Medical Education department was very supportive.” The administration claimed that students mostly had positive feedback regarding faculty’s performance because regardless of the quality of teaching, the students were happy to stay at home and take lectures (Fig.  2 ).

Differences between Online and On-Campus Teaching : The participants identified various differences between online and on-campus teaching. Physical presence, eye contact, and gestures were missing in online teaching, and non-verbal communication was lacking, leading to less effective teaching. However, the faculty believed that blended learning could be utilized post-COVID.

Funding for Online Teaching : The participants agreed that not much funding was required for implementing online teaching during the COVID-19 pandemic. The principal mentioned that the savings from electricity and transportation balanced the funding requirements. However, the DSA suggested that funding was required for cameras, Zoom, webinars, and laptop devices, claiming that the “the medical education department was not equipped initially , and they had to purchase webinars and Zoom hours.”

Satisfaction with Study Guides and Planners : Overall, the participants were satisfied with the faculty’s job in a short time to take over as compared to other institutes, but there was some variation in teaching quality as reported by the students.

Medical Education’s Preparedness for Online Teaching : Participants held divergent opinions regarding Medical Education’s preparedness for online teaching. The principal advocated for greater availability of teaching technologies. Conversely, the DSA emphasized the department’s focus on faculty training and suggested an increased emphasis on student training. Faculty members acknowledged effective training provided by the department but noted areas for improvement, particularly in the admission process during COVID-19 (Fig.  2 b). Despite this, participants generally agreed that the admission process posed minimal challenges. The successful implementation of multiple mini-interviews (MMI) online allowed for more efficient interviewing of students. However, there was a noted absence of assessment for non-verbal communication.

Progress Monitoring of Online Teaching : According to the participants, progress monitoring of online teaching was carried out based on feedback from both students and faculty, while any technological issues were handled by experts.

Students feedback for faculty

Students feedback for faculty teaching during online and hybrid sessions are shown in Table  2 . Students rated anatomy teaching in online sessions significantly better than hybrid sessions. These students found that learning sessions were more student-centered with supporting online classes ( p  < 0.05). They also reported that tutorial sessions improved their problem-solving skills. Teachers were noted to be helpful in stress management during the pandemic, with the college employing proper counselors to cater to the needs of students who required assistance.

However, faculty rating by students in physiology and biochemistry were comparable during online and hybrid teaching sessions. These students expressed satisfaction with the availability of proper infrastructure, resources in the library and IT, as well as support from faculty and staff. They were also content with the teaching and assessment environment provided. Additionally, students found co-curricular activities to be motivating.

Messages for students and faculty

The participants had various messages for students and faculty. The principal suggested that technology is here to stay, and we have to develop these skills to survive, highlighting that “technology has to stay , so better get tech-savvy” . The DSA suggested that faculty should focus on delivery, and students should try to be good doctors and better serve humanity. The faculty suggested that students and faculty should be well-prepared for online teaching, and the medical education department should conduct workshops on online teaching and learning at least twice annually (Fig.  3 ).

Feedback from Administration and Faculty. a. Qualitative responses of FGD by Administrators: The response from administrators were highest regarding need for improvement in the online system followed by message given to the students and more focused on funding required for it. b. Qualitative responses of FGD by Faculty Basic Health Sciences: The faculty response was mostly focused on training of faculty and network issues which had to be fixed on urgent basis. However online teaching was appreciated as it helps them to be tech savvy

Intended outcomes

Both the 2019-20 and 2020-21 batches underwent assessment through module and professional examinations, which were conducted online as well as on campus. The admission merit of both batches was analyzed alongside their academic performance. Additionally, the examination papers prepared by the faculty for module examinations in basic sciences underwent assessment for reliability by Cronbach’s alpha (Table  3 ).

The quality of teaching delivery by the faculty was also evaluated through the review of recorded YouTube lectures. Despite the 2020-21 batch having lower admission merit compared to the 2019-20 batch, which was taught entirely online, the former, taught in a hybrid format (partly online and partly on campus), demonstrated significantly better performance in module and professional examinations for anatomy, physiology, and biochemistry. The quality of teaching and examination papers was found to be consistent both online and on campus. However, factors such as low motivation, mental stress due to the pandemic’s effects on students and their families, ineffective proctoring mechanisms, and the absence of physical teacher presence in online classes contributed to the lower performance of the batch taught entirely online. The lack of co-curricular activities also played a role in this outcome.

Un-intentioned outcomes

The pandemic-induced shift to online teaching resulted in comprehensive teacher training for blended learning sessions and courses. This equipped educators to develop and deliver online courses as supplemental resources for students. Moreover, students gained proficiency in online teaching and assessment techniques, enabling the incorporation of low-stakes examinations on Learning Management Systems (LMS) such as Moodle. This approach not only streamlines the process but also offers flexibility for both educators and students, ultimately enhancing the teaching and learning experience.

Short-term implications

The batch that experienced solely online teaching during the pandemic came to appreciate the value of attending medical school, recognizing its role not only in providing quality education but also in fostering co-curricular activities, problem-solving skills, team building, leadership abilities, and offering counseling support when needed. Additionally, faculty members recognized the importance of being technologically proficient and the benefits of blended learning, which can encourage students to take more responsibility for their studies. There was a recognized need for strengthening the medical education department in terms of online teaching and providing regular faculty training. The 2019–20 batch achieved a passing rate of 92.58% in the university professional examination, while the 2020–21 batch scored even higher with a passing rate of 98.16%. Faculty involved in teaching and assessment noted that the professional papers in basic sciences for the 2019–20 batch were comparatively easier than those for the 2020–21 batch.

Long-term implications

The online program has proven to be an effective alternative to on-campus teaching, particularly in a blended format. Both batches showed improved performance over the next two years, achieving impressive results of 97–98% in 2021 and 2022. However, the true measure of success will be observed when these batches graduate and begin working in hospitals, providing insight into the long-term impact of the online teaching approach.

Program impact

The online program proved successful in hybrid (blended) form, albeit with certain limitations evident in the results, particularly for the total online approach.

Program effectiveness

The majority of students demonstrated good performance, particularly in hybrid learning methods, underscoring the importance of incorporating blended learning approaches that combine both asynchronous and synchronous forms.

Program sustainability

The program is integrated into the Learning Management Systems (LMS), with additional tools like webinars and Zoom purchased as needed. This expenditure does not impose a significant financial burden on the institute, making the program sustainable in its current or enhanced form.

Ease of adoption

The program is readily accessible and cost-effective. However, its sustainability and effectiveness rely on thorough training of faculty and students, coupled with adequate support from the administration and medical education department. This ensures a cost-effective and sustainable model that can be easily replicated by other institutions.

Quantitative results

Analysis of module examinations (table  3 ).

Reliability of module assessment papers in subjects of anatomy, physiology and biochemistry was determined using Cronbach’s alpha during hybrid sessions and online sessions. The data shows different reliability of papers across various disciplines. The table shows reliability of assessments were low in the beginning of COVID- 19 Pandemic i.e. in 2019–2020 (totally online) but improved with passage of time in the basic health sciences subjects in 2020-21 (hybrid) except for biochemistry which shows more reliability of papers in online tests compared to hybrid.

Comparison of admission scores, internal assessment scores, and professional examination scores (table  4 )

The admission merit, particularly MDCAT scores, and final merit were significantly higher for the online batch (2019–2020) compared to the hybrid batch (2020–2021) P value 0.01. Internal assessments of anatomy improved significantly in the hybrid teaching batch compared to the online batch, while physiology and biochemistry remained comparable between the two batches. First professional results of anatomy and physiology showed significant improvement in the hybrid teaching batch, while biochemistry results remained comparable between the two batches.

Overall, the hybrid teaching approach resulted in improved outcomes in certain areas compared to total online teaching, particularly in internal assessments and first professional examination results.

The study reports an in-depth mixed method to evaluate and compare the online versus hybrid model of teaching during COVID-19 utilizing the CIPP model. The context, input, process, and product were assessed during 2019–20 and 2020–21 by obtaining perspectives from students, faculty who taught them, and administrators. The context was the urgent need of transition to online teaching to maintain the continuity of education and academics during the COVID-19 pandemic. This transition occurred globally at almost every institution in developed as well as developing countries [ 8 ]. The rapid training provided to faculty and students on online teaching within a week or two was appreciated by all stakeholders. However, the students highlighted the lack of interaction between students and faculty during webinar sessions. This could be attributed to the one-way flow of information via lectures and the inability to see the students physically. Practical sessions were also only demonstrated, and students were unable to perform them. A study conducted in Shiraz, Iran, found similar findings [ 9 ]. Inadequate internet connectivity, especially in peripheral areas of the country, was the main issue encountered by the students. This led to anxiety among them during assessments. A study from India also highlighted some common downsides to remote teaching from the perspective of undergraduate medical students, including technical difficulties, ease of distraction, and some staff being poorly versed in the technologies used [ 10 ]. The major obstacles have included delivering online teaching content as well as adapting means of assessment in such unforeseen circumstances [ 11 ]. The alternative approach taken by Imperial College London was to introduce an open book examination (OBE), in which the questions were designed in such a way that students were allowed to use internet sources during the examination. The perception of 2721 medical students across 39 medical schools in the UK revealed flexibility as an advantage and internet connection as a barrier to online education [ 12 ]. OBE was implemented for internal assessments during COVID-19 in our setup but was not done for professional examinations. The students commented on the advantages of online teaching more, as traveling was not required, and they could study from home. Faculty coordination was improved, and they were trained in blended learning [ 13 ]. Similarly, students’ knowledge, attitudes, and practices were reported by Noreen et al. (2020) during COVID-19 in Pakistani medical schools, supporting our study [ 14 ]. Quantitative analysis showed that internal assessment and modular examination papers were equally reliable in all basic subjects in the first-year MBBS. However, the admission merit of group A was higher than that of group B, but the scores of internal assessments and professional examinations were higher for group B students compared to group (A) There could be multiple reasons for this. As shown in our results, the admission criteria for group A were totally based on PMDC criteria, where no marks were allocated to the medical colleges for interviews, while in group B, 20% of marks were allocated to them for interviews due to the change from PMDC to PMC. Moreover, the medical college changed its attendance and assessment criteria from 75% attendance and a 50% assessment cutoff to be eligible for professional examinations for group A to 90% attendance and a 60% assessment cutoff for the years 2020–21 for group (B) A systematic review of the academic performance of students during COVID-19 reported variable results, spanning from low to high [ 15 ]. Similarly, a study by Sulail Fatima et al. (2021) conducted in Karachi, Pakistan, reported low academic performance in module assessments conducted online versus high scores in face-to-face assessments, which supports our study [ 16 ]. Shamsa et al. (2018) evaluated the quality of school programs using the CIPP model, which revealed significant findings that were recommended to be improved [ 17 ]. Similar studies were carried out in Pakistan to evaluate the continuous development program for family physicians and the bioethics diploma program [ 18 , 19 , 20 ]. The effect of the pandemic on medical training will be analyzed after these students graduate and start practicing. The workplace-based assessment will provide a clear picture of the online teaching during COVID-19. However, it has broadened the horizons of training by integrating asynchronous and synchronous teaching models. Telemedicine and flipped classrooms are now more frequently utilized for content delivery and patient care than before, with more advantages compared to conventional archetypes. These will become more refined with the passage of time with the integration of artificial intelligence (AI) like Chat-GPT and research rabbits. This has posed challenges for faculty to identify the learning methods that can be successfully integrated into their curriculum.

The CIPP model program evaluation highlighted the challenges encountered by both students and faculty during total online teaching, shedding light on gaps in students’ knowledge and skills. Furthermore, it offers guidance to administrators and program directors to pinpoint areas needing improvement, facilitating the implementation of necessary changes. While students valued the hybrid model for its engaging teacher-student interaction, faculty members favored online teaching strategies and proposed the future use of blended learning. The administration recognized the faculty’s swift transition to online teaching and their commendable performance. However, they emphasized the necessity of faculty development workshops on blended learning and strengthening the medical education department. Based on our study, we recommend:

Blended learning strategies (Both synchronous and asynchronous should be used for teaching and learning as it generates sense of responsibility amongst the students, create interest and generate team work.

Workshops for blended learning techniques for faculty should be done frequently.

Medical education department should be equipped to facilitate online teaching /learning.

Feedback of the students should be taken after each module to cater their needs and improve the curriculum.

Data availability

All of the relevant raw data of this study will be available from Prof. Dr. Anila Jaleel (corresponding author) for scientists who wish to use them for non- commercial basis.

Abbreviations

Context, Input, Process and Product

Focused group discussion

Pakistan Medical Commission

Pakistan Medical and Dental Council

Institutional review Board

Small group discussion

Problem based learning

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Jaleel, A., Iqbal, S.P., Cheema, K.M. et al. Navigating undergraduate medical education: a comparative evaluation of a fully online versus a hybrid model. BMC Med Educ 24 , 895 (2024). https://doi.org/10.1186/s12909-024-05865-6

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Received : 18 September 2023

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DOI : https://doi.org/10.1186/s12909-024-05865-6

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