research proposal about new normal education

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• Published: 15 February 2018

Blended learning: the new normal and emerging technologies

• Charles Dziuban 1 ,
• Charles R. Graham 2 ,
• Patsy D. Moskal   ORCID: orcid.org/0000-0001-6376-839X 1 ,
• Anders Norberg 3 &
• Nicole Sicilia 1  

International Journal of Educational Technology in Higher Education volume  15 , Article number:  3 ( 2018 ) Cite this article

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This study addressed several outcomes, implications, and possible future directions for blended learning (BL) in higher education in a world where information communication technologies (ICTs) increasingly communicate with each other. In considering effectiveness, the authors contend that BL coalesces around access, success, and students’ perception of their learning environments. Success and withdrawal rates for face-to-face and online courses are compared to those for BL as they interact with minority status. Investigation of student perception about course excellence revealed the existence of robust if-then decision rules for determining how students evaluate their educational experiences. Those rules were independent of course modality, perceived content relevance, and expected grade. The authors conclude that although blended learning preceded modern instructional technologies, its evolution will be inextricably bound to contemporary information communication technologies that are approximating some aspects of human thought processes.

Introduction

Blended learning and research issues.

Blended learning (BL), or the integration of face-to-face and online instruction (Graham 2013 ), is widely adopted across higher education with some scholars referring to it as the “new traditional model” (Ross and Gage 2006 , p. 167) or the “new normal” in course delivery (Norberg et al. 2011 , p. 207). However, tracking the accurate extent of its growth has been challenging because of definitional ambiguity (Oliver and Trigwell 2005 ), combined with institutions’ inability to track an innovative practice, that in many instances has emerged organically. One early nationwide study sponsored by the Sloan Consortium (now the Online Learning Consortium) found that 65.2% of participating institutions of higher education (IHEs) offered blended (also termed hybrid ) courses (Allen and Seaman 2003 ). A 2008 study, commissioned by the U.S. Department of Education to explore distance education in the U.S., defined BL as “a combination of online and in-class instruction with reduced in-class seat time for students ” (Lewis and Parsad 2008 , p. 1, emphasis added). Using this definition, the study found that 35% of higher education institutions offered blended courses, and that 12% of the 12.2 million documented distance education enrollments were in blended courses.

The 2017 New Media Consortium Horizon Report found that blended learning designs were one of the short term forces driving technology adoption in higher education in the next 1–2 years (Adams Becker et al. 2017 ). Also, blended learning is one of the key issues in teaching and learning in the EDUCAUSE Learning Initiative’s 2017 annual survey of higher education (EDUCAUSE 2017 ). As institutions begin to examine BL instruction, there is a growing research interest in exploring the implications for both faculty and students. This modality is creating a community of practice built on a singular and pervasive research question, “How is blended learning impacting the teaching and learning environment?” That question continues to gain traction as investigators study the complexities of how BL interacts with cognitive, affective, and behavioral components of student behavior, and examine its transformation potential for the academy. Those issues are so compelling that several volumes have been dedicated to assembling the research on how blended learning can be better understood (Dziuban et al. 2016 ; Picciano et al. 2014 ; Picciano and Dziuban 2007 ; Bonk and Graham 2007 ; Kitchenham 2011 ; Jean-François 2013 ; Garrison and Vaughan 2013 ) and at least one organization, the Online Learning Consortium, sponsored an annual conference solely dedicated to blended learning at all levels of education and training (2004–2015). These initiatives address blended learning in a wide variety of situations. For instance, the contexts range over K-12 education, industrial and military training, conceptual frameworks, transformational potential, authentic assessment, and new research models. Further, many of these resources address students’ access, success, withdrawal, and perception of the degree to which blended learning provides an effective learning environment.

Currently the United States faces a widening educational gap between our underserved student population and those communities with greater financial and technological resources (Williams 2016 ). Equal access to education is a critical need, one that is particularly important for those in our underserved communities. Can blended learning help increase access thereby alleviating some of the issues faced by our lower income students while resulting in improved educational equality? Although most indicators suggest “yes” (Dziuban et al. 2004 ), it seems that, at the moment, the answer is still “to be determined.” Quality education presents a challenge, evidenced by many definitions of what constitutes its fundamental components (Pirsig 1974 ; Arum et al. 2016 ). Although progress has been made by initiatives, such as, Quality Matters ( 2016 ), the OLC OSCQR Course Design Review Scorecard developed by Open SUNY (Open SUNY n.d. ), the Quality Scorecard for Blended Learning Programs (Online Learning Consortium n.d. ), and SERVQUAL (Alhabeeb 2015 ), the issue is by no means resolved. Generally, we still make quality education a perceptual phenomenon where we ascribe that attribute to a course, educational program, or idea, but struggle with precisely why we reached that decision. Searle ( 2015 ), summarizes the problem concisely arguing that quality does not exist independently, but is entirely observer dependent. Pirsig ( 1974 ) in his iconic volume on the nature of quality frames the context this way,

“There is such thing as Quality, but that as soon as you try to define it, something goes haywire. You can’t do it” (p. 91).

Therefore, attempting to formulate a semantic definition of quality education with syntax-based metrics results in what O’Neil (O'Neil 2017 ) terms surrogate models that are rough approximations and oversimplified. Further, the derived metrics tend to morph into goals or benchmarks, losing their original measurement properties (Goodhart 1975 ).

Information communication technologies in society and education

Blended learning forces us to consider the characteristics of digital technology, in general, and information communication technologies (ICTs), more specifically. Floridi ( 2014 ) suggests an answer proffered by Alan Turing: that digital ICTs can process information on their own, in some sense just as humans and other biological life. ICTs can also communicate information to each other, without human intervention, but as linked processes designed by humans. We have evolved to the point where humans are not always “in the loop” of technology, but should be “on the loop” (Floridi 2014 , p. 30), designing and adapting the process. We perceive our world more and more in informational terms, and not primarily as physical entities (Floridi 2008 ). Increasingly, the educational world is dominated by information and our economies rest primarily on that asset. So our world is also blended, and it is blended so much that we hardly see the individual components of the blend any longer. Floridi ( 2014 ) argues that the world has become an “infosphere” (like biosphere) where we live as “inforgs.” What is real for us is shifting from the physical and unchangeable to those things with which we can interact.

Floridi also helps us to identify the next blend in education, involving ICTs, or specialized artificial intelligence (Floridi 2014 , 25; Norberg 2017 , 65). Learning analytics, adaptive learning, calibrated peer review, and automated essay scoring (Balfour 2013 ) are advanced processes that, provided they are good interfaces, can work well with the teacher— allowing him or her to concentrate on human attributes such as being caring, creative, and engaging in problem-solving. This can, of course, as with all technical advancements, be used to save resources and augment the role of the teacher. For instance, if artificial intelligence can be used to work along with teachers, allowing them more time for personal feedback and mentoring with students, then, we will have made a transformational breakthrough. The Edinburg University manifesto for teaching online says bravely, “Automation need not impoverish education – we welcome our robot colleagues” (Bayne et al. 2016 ). If used wisely, they will teach us more about ourselves, and about what is truly human in education. This emerging blend will also affect curricular and policy questions, such as the what? and what for? The new normal for education will be in perpetual flux. Floridi’s ( 2014 ) philosophy offers us tools to understand and be in control and not just sit by and watch what happens. In many respects, he has addressed the new normal for blended learning.

Literature of blended learning

A number of investigators have assembled a comprehensive agenda of transformative and innovative research issues for blended learning that have the potential to enhance effectiveness (Garrison and Kanuka 2004 ; Picciano 2009 ). Generally, research has found that BL results in improvement in student success and satisfaction, (Dziuban and Moskal 2011 ; Dziuban et al. 2011 ; Means et al. 2013 ) as well as an improvement in students’ sense of community (Rovai and Jordan 2004 ) when compared with face-to-face courses. Those who have been most successful at blended learning initiatives stress the importance of institutional support for course redesign and planning (Moskal et al. 2013 ; Dringus and Seagull 2015 ; Picciano 2009 ; Tynan et al. 2015 ). The evolving research questions found in the literature are long and demanding, with varied definitions of what constitutes “blended learning,” facilitating the need for continued and in-depth research on instructional models and support needed to maximize achievement and success (Dringus and Seagull 2015 ; Bloemer and Swan 2015 ).

Educational access

The lack of access to educational technologies and innovations (sometimes termed the digital divide) continues to be a challenge with novel educational technologies (Fairlie 2004 ; Jones et al. 2009 ). One of the promises of online technologies is that they can increase access to nontraditional and underserved students by bringing a host of educational resources and experiences to those who may have limited access to on-campus-only higher education. A 2010 U.S. report shows that students with low socioeconomic status are less likely to obtain higher levels of postsecondary education (Aud et al. 2010 ). However, the increasing availability of distance education has provided educational opportunities to millions (Lewis and Parsad 2008 ; Allen et al. 2016 ). Additionally, an emphasis on open educational resources (OER) in recent years has resulted in significant cost reductions without diminishing student performance outcomes (Robinson et al. 2014 ; Fischer et al. 2015 ; Hilton et al. 2016 ).

Unfortunately, the benefits of access may not be experienced evenly across demographic groups. A 2015 study found that Hispanic and Black STEM majors were significantly less likely to take online courses even when controlling for academic preparation, socioeconomic status (SES), citizenship, and English as a second language (ESL) status (Wladis et al. 2015 ). Also, questions have been raised about whether the additional access afforded by online technologies has actually resulted in improved outcomes for underserved populations. A distance education report in California found that all ethnic minorities (except Asian/Pacific Islanders) completed distance education courses at a lower rate than the ethnic majority (California Community Colleges Chancellor’s Office 2013 ). Shea and Bidjerano ( 2014 , 2016 ) found that African American community college students who took distance education courses completed degrees at significantly lower rates than those who did not take distance education courses. On the other hand, a study of success factors in K-12 online learning found that for ethnic minorities, only 1 out of 15 courses had significant gaps in student test scores (Liu and Cavanaugh 2011 ). More research needs to be conducted, examining access and success rates for different populations, when it comes to learning in different modalities, including fully online and blended learning environments.

Framing a treatment effect

Over the last decade, there have been at least five meta-analyses that have addressed the impact of blended learning environments and its relationship to learning effectiveness (Zhao et al. 2005 ; Sitzmann et al. 2006 ; Bernard et al. 2009 ; Means et al. 2010 , 2013 ; Bernard et al. 2014 ). Each of these studies has found small to moderate positive effect sizes in favor of blended learning when compared to fully online or traditional face-to-face environments. However, there are several considerations inherent in these studies that impact our understanding the generalizability of outcomes.

Dziuban and colleagues (Dziuban et al. 2015 ) analyzed the meta-analyses conducted by Means and her colleagues (Means et al. 2013 ; Means et al. 2010 ), concluding that their methods were impressive as evidenced by exhaustive study inclusion criteria and the use of scale-free effect size indices. The conclusion, in both papers, was that there was a modest difference in multiple outcome measures for courses featuring online modalities—in particular, blended courses. However, with blended learning especially, there are some concerns with these kinds of studies. First, the effect sizes are based on the linear hypothesis testing model with the underlying assumption that the treatment and the error terms are uncorrelated, indicating that there is nothing else going on in the blending that might confound the results. Although the blended learning articles (Means et al. 2010 ) were carefully vetted, the assumption of independence is tenuous at best so that these meta-analysis studies must be interpreted with extreme caution.

There is an additional concern with blended learning as well. Blends are not equivalent because of the manner on which they are configured. For instance, a careful reading of the sources used in the Means, et al. papers will identify, at minimum, the following blending techniques: laboratory assessments, online instruction, e-mail, class web sites, computer laboratories, mapping and scaffolding tools, computer clusters, interactive presentations and e-mail, handwriting capture, evidence-based practice, electronic portfolios, learning management systems, and virtual apparatuses. These are not equivalent ways in which to configure courses, and such nonequivalence constitutes the confounding we describe. We argue here that, in actuality, blended learning is a general construct in the form of a boundary object (Star and Griesemer 1989 ) rather than a treatment effect in the statistical sense. That is, an idea or concept that can support a community of practice, but is weakly defined fostering disagreement in the general group. Conversely, it is stronger in individual constituencies. For instance, content disciplines (i.e. education, rhetoric, optics, mathematics, and philosophy) formulate a more precise definition because of commonly embraced teaching and learning principles. Quite simply, the situation is more complicated than that, as Leonard Smith ( 2007 ) says after Tolstoy,

“All linear models resemble each other, each non nonlinear system is unique in its own way” (p. 33).

This by no means invalidates these studies, but effect size associated with blended learning should be interpreted with caution where the impact is evaluated within a particular learning context.

Study objectives

This study addressed student access by examining success and withdrawal rates in the blended learning courses by comparing them to face-to-face and online modalities over an extended time period at the University of Central Florida. Further, the investigators sought to assess the differences in those success and withdrawal rates with the minority status of students. Secondly, the investigators examined the student end-of-course ratings of blended learning and other modalities by attempting to develop robust if-then decision rules about what characteristics of classes and instructors lead students to assign an “excellent” value to their educational experience. Because of the high stakes nature of these student ratings toward faculty promotion, awards, and tenure, they act as a surrogate measure for instructional quality. Next, the investigators determined the conditional probabilities for students conforming to the identified rule cross-referenced by expected grade, the degree to which they desired to take the course, and course modality.

Student grades by course modality were recoded into a binary variable with C or higher assigned a value of 1, and remaining values a 0. This was a declassification process that sacrificed some specificity but compensated for confirmation bias associated with disparate departmental policies regarding grade assignment. At the measurement level this was an “on track to graduation index” for students. Withdrawal was similarly coded by the presence or absence of its occurrence. In each case, the percentage of students succeeding or withdrawing from blended, online or face-to-face courses was calculated by minority and non-minority status for the fall 2014 through fall 2015 semesters.

Next, a classification and regression tree (CART) analysis (Brieman et al. 1984 ) was performed on the student end-of-course evaluation protocol ( Appendix 1 ). The dependent measure was a binary variable indicating whether or not a student assigned an overall rating of excellent to his or her course experience. The independent measures in the study were: the remaining eight rating items on the protocol, college membership, and course level (lower undergraduate, upper undergraduate, and graduate). Decision trees are efficient procedures for achieving effective solutions in studies such as this because with missing values imputation may be avoided with procedures such as floating methods and the surrogate formation (Brieman et al. 1984 , Olshen et al. 1995 ). For example, a logistic regression method cannot efficiently handle all variables under consideration. There are 10 independent variables involved here; one variable has three levels, another has nine, and eight have five levels each. This means the logistic regression model must incorporate more than 50 dummy variables and an excessively large number of two-way interactions. However, the decision-tree method can perform this analysis very efficiently, permitting the investigator to consider higher order interactions. Even more importantly, decision trees represent appropriate methods in this situation because many of the variables are ordinally scaled. Although numerical values can be assigned to each category, those values are not unique. However, decision trees incorporate the ordinal component of the variables to obtain a solution. The rules derived from decision trees have an if-then structure that is readily understandable. The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood. The procedure produces tree-like rule structures that predict outcomes.

The model-building procedure for predicting overall instructor rating

For this study, the investigators used the CART method (Brieman et al. 1984 ) executed with SPSS 23 (IBM Corp 2015 ). Because of its strong variance-sharing tendencies with the other variables, the dependent measure for the analysis was the rating on the item Overall Rating of the Instructor , with the previously mentioned indicator variables (college, course level, and the remaining 8 questions) on the instrument. Tree methods are recursive, and bisect data into subgroups called nodes or leaves. CART analysis bases itself on: data splitting, pruning, and homogeneous assessment.

Splitting the data into two (binary) subsets comprises the first stage of the process. CART continues to split the data until the frequencies in each subset are either very small or all observations in a subset belong to one category (e.g., all observations in a subset have the same rating). Usually the growing stage results in too many terminate nodes for the model to be useful. CART solves this problem using pruning methods that reduce the dimensionality of the system.

The final stage of the analysis involves assessing homogeneousness in growing and pruning the tree. One way to accomplish this is to compute the misclassification rates. For example, a rule that produces a .95 probability that an instructor will receive an excellent rating has an associated error of 5.0%.

Implications for using decision trees

Although decision-tree techniques are effective for analyzing datasets such as this, the reader should be aware of certain limitations. For example, since trees use ranks to analyze both ordinal and interval variables, information can be lost. However, the most serious weakness of decision tree analysis is that the results can be unstable because small initial variations can lead to substantially different solutions.

For this study model, these problems were addressed with the k-fold cross-validation process. Initially the dataset was partitioned randomly into 10 subsets with an approximately equal number of records in each subset. Each cohort is used as a test partition, and the remaining subsets are combined to complete the function. This produces 10 models that are all trained on different subsets of the original dataset and where each has been used as the test partition one time only.

Although computationally dense, CART was selected as the analysis model for a number of reasons— primarily because it provides easily interpretable rules that readers will be able evaluate in their particular contexts. Unlike many other multivariate procedures that are even more sensitive to initial estimates and require a good deal of statistical sophistication for interpretation, CART has an intuitive resonance with researcher consumers. The overriding objective of our choice of analysis methods was to facilitate readers’ concentration on our outcomes rather than having to rely on our interpretation of the results.

Institution-level evaluation: Success and withdrawal

The University of Central Florida (UCF) began a longitudinal impact study of their online and blended courses at the start of the distributed learning initiative in 1996. The collection of similar data across multiple semesters and academic years has allowed UCF to monitor trends, assess any issues that may arise, and provide continual support for both faculty and students across varying demographics. Table  1 illustrates the overall success rates in blended, online and face-to-face courses, while also reporting their variability across minority and non-minority demographics.

While success (A, B, or C grade) is not a direct reflection of learning outcomes, this overview does provide an institutional level indication of progress and possible issues of concern. BL has a slight advantage when looking at overall success and withdrawal rates. This varies by discipline and course, but generally UCF’s blended modality has evolved to be the best of both worlds, providing an opportunity for optimizing face-to-face instruction through the effective use of online components. These gains hold true across minority status. Reducing on-ground time also addresses issues that impact both students and faculty such as parking and time to reach class. In addition, UCF requires faculty to go through faculty development tailored to teaching in either blended or online modalities. This 8-week faculty development course is designed to model blended learning, encouraging faculty to redesign their course and not merely consider blended learning as a means to move face-to-face instructional modules online (Cobb et al. 2012 ; Lowe 2013 ).

Withdrawal (Table  2 ) from classes impedes students’ success and retention and can result in delayed time to degree, incurred excess credit hour fees, or lost scholarships and financial aid. Although grades are only a surrogate measure for learning, they are a strong predictor of college completion. Therefore, the impact of any new innovation on students’ grades should be a component of any evaluation. Once again, the blended modality is competitive and in some cases results in lower overall withdrawal rates than either fully online or face-to-face courses.

The students’ perceptions of their learning environments

Other potentially high-stakes indicators can be measured to determine the impact of an innovation such as blended learning on the academy. For instance, student satisfaction and attitudes can be measured through data collection protocols, including common student ratings, or student perception of instruction instruments. Given that those ratings often impact faculty evaluation, any negative reflection can derail the successful implementation and scaling of an innovation by disenfranchised instructors. In fact, early online and blended courses created a request by the UCF faculty senate to investigate their impact on faculty ratings as compared to face-to-face sections. The UCF Student Perception of Instruction form is released automatically online through the campus web portal near the end of each semester. Students receive a splash page with a link to each course’s form. Faculty receive a scripted email that they can send to students indicating the time period that the ratings form will be available. The forms close at the beginning of finals week. Faculty receive a summary of their results following the semester end.

The instrument used for this study was developed over a ten year period by the faculty senate of the University of Central Florida, recognizing the evolution of multiple course modalities including blended learning. The process involved input from several constituencies on campus (students, faculty, administrators, instructional designers, and others), in attempt to provide useful formative and summative instructional information to the university community. The final instrument was approved by resolution of the senate and, currently, is used across the university. Students’ rating of their classes and instructors comes with considerable controversy and disagreement with researchers aligning themselves on both sides of the issue. Recently, there have been a number of studies criticizing the process (Uttl et al. 2016 ; Boring et al. 2016 ; & Stark and Freishtat 2014 ). In spite of this discussion, a viable alternative has yet to emerge in higher education. So in the foreseeable future, the process is likely to continue. Therefore, with an implied faculty senate mandate this study was initiated by this team of researchers.

Prior to any analysis of the item responses collected in this campus-wide student sample, the psychometric quality (domain sampling) of the information yielded by the instrument was assessed. Initially, the reliability (internal consistency) was derived using coefficient alpha (Cronbach 1951 ). In addition, Guttman ( 1953 ) developed a theorem about item properties that leads to evidence about the quality of one’s data, demonstrating that as the domain sampling properties of items improve, the inverse of the correlation matrix among items will approach a diagonal. Subsequently, Kaiser and Rice ( 1974 ) developed the measure of sampling adequacy (MSA) that is a function of the Guttman Theorem. The index has an upper bound of one with Kaiser offering some decision rules for interpreting the value of MSA. If the value of the index is in the .80 to .99 range, the investigator has evidence of an excellent domain sample. Values in the .70s signal an acceptable result, and those in the .60s indicate data that are unacceptable. Customarily, the MSA has been used for data assessment prior to the application of any dimensionality assessments. Computation of the MSA value gave the investigators a benchmark for the construct validity of the items in this study. This procedure has been recommended by Dziuban and Shirkey ( 1974 ) prior to any latent dimension analysis and was used with the data obtained for this study. The MSA for the current instrument was .98 suggesting excellent domain sampling properties with an associated alpha reliability coefficient of .97 suggesting superior internal consistency. The psychometric properties of the instrument were excellent with both measures.

The online student ratings form presents an electronic data set each semester. These can be merged across time to create a larger data set of completed ratings for every course across each semester. In addition, captured data includes course identification variables including prefix, number, section and semester, department, college, faculty, and class size. The overall rating of effectiveness is used most heavily by departments and faculty in comparing across courses and modalities (Table  3 ).

The finally derived tree (decision rules) included only three variables—survey items that asked students to rate the instructor’s effectiveness at:

Helping students achieve course objectives,

Creating an environment that helps students learn, and

Communicating ideas and information.

None of the demographic variables associated with the courses contributed to the final model. The final rule specifies that if a student assigns an excellent rating to those three items, irrespective of their status on any other condition, the probability is .99 that an instructor will receive an overall rating of excellent. The converse is true as well. A poor rating on all three of those items will lead to a 99% chance of an instructor receiving an overall rating of poor.

Tables  4 , 5 and 6 present a demonstration of the robustness of the CART rule for variables on which it was not developed: expected course grade, desire to take the course and modality.

In each case, irrespective of the marginal probabilities, those students conforming to the rule have a virtually 100% chance of seeing the course as excellent. For instance, 27% of all students expecting to fail assigned an excellent rating to their courses, but when they conformed to the rule the percentage rose to 97%. The same finding is true when students were asked about their desire to take the course with those who strongly disagreed assigning excellent ratings to their courses 26% of the time. However, for those conforming to the rule, that category rose to 92%. When course modality is considered in the marginal sense, blended learning is rated as the preferred choice. However, from Table  6 we can observe that the rule equates student assessment of their learning experiences. If they conform to the rule, they will see excellence.

This study addressed increasingly important issues of student success, withdrawal and perception of the learning environment across multiple course modalities. Arguably these components form the crux of how we will make more effective decisions about how blended learning configures itself in the new normal. The results reported here indicate that blending maintains or increases access for most student cohorts and produces improved success rates for minority and non-minority students alike. In addition, when students express their beliefs about the effectiveness of their learning environments, blended learning enjoys the number one rank. However, upon more thorough analysis of key elements students view as important in their learning, external and demographic variables have minimal impact on those decisions. For example college (i.e. discipline) membership, course level or modality, expected grade or desire to take a particular course have little to do with their course ratings. The characteristics they view as important relate to clear establishment and progress toward course objectives, creating an effective learning environment and the instructors’ effective communication. If in their view those three elements of a course are satisfied they are virtually guaranteed to evaluate their educational experience as excellent irrespective of most other considerations. While end of course rating protocols are summative the three components have clear formative characteristics in that each one is directly related to effective pedagogy and is responsive to faculty development through units such as the faculty center for teaching and learning. We view these results as encouraging because they offer potential for improving the teaching and learning process in an educational environment that increases the pressure to become more responsive to contemporary student lifestyles.

Clearly, in this study we are dealing with complex adaptive systems that feature the emergent property. That is, their primary agents and their interactions comprise an environment that is more than the linear combination of their individual elements. Blending learning, by interacting with almost every aspect of higher education, provides opportunities and challenges that we are not able to fully anticipate.

This pedagogy alters many assumptions about the most effective way to support the educational environment. For instance, blending, like its counterpart active learning, is a personal and individual phenomenon experienced by students. Therefore, it should not be surprising that much of what we have called blended learning is, in reality, blended teaching that reflects pedagogical arrangements. Actually, the best we can do for assessing impact is to use surrogate measures such as success, grades, results of assessment protocols, and student testimony about their learning experiences. Whether or not such devices are valid indicators remains to be determined. We may be well served, however, by changing our mode of inquiry to blended teaching.

Additionally, as Norberg ( 2017 ) points out, blended learning is not new. The modality dates back, at least, to the medieval period when the technology of textbooks was introduced into the classroom where, traditionally, the professor read to the students from the only existing manuscript. Certainly, like modern technologies, books were disruptive because they altered the teaching and learning paradigm. Blended learning might be considered what Johnson describes as a slow hunch (2010). That is, an idea that evolved over a long period of time, achieving what Kaufmann ( 2000 ) describes as the adjacent possible – a realistic next step occurring in many iterations.

The search for a definition for blended learning has been productive, challenging, and, at times, daunting. The definitional continuum is constrained by Oliver and Trigwell ( 2005 ) castigation of the concept for its imprecise vagueness to Sharpe et al.’s ( 2006 ) notion that its definitional latitude enhances contextual relevance. Both extremes alter boundaries such as time, place, presence, learning hierarchies, and space. The disagreement leads us to conclude that Lakoff’s ( 2012 ) idealized cognitive models i.e. arbitrarily derived concepts (of which blended learning might be one) are necessary if we are to function effectively. However, the strong possibility exists that blended learning, like quality, is observer dependent and may not exist outside of our perceptions of the concept. This, of course, circles back to the problem of assuming that blending is a treatment effect for point hypothesis testing and meta-analysis.

Ultimately, in this article, we have tried to consider theoretical concepts and empirical findings about blended learning and their relationship to the new normal as it evolves. Unfortunately, like unresolved chaotic solutions, we cannot be sure that there is an attractor or that it will be the new normal. That being said, it seems clear that blended learning is the harbinger of substantial change in higher education and will become equally impactful in K-12 schooling and industrial training. Blended learning, because of its flexibility, allows us to maximize many positive education functions. If Floridi ( 2014 ) is correct and we are about to live in an environment where we are on the communication loop rather than in it, our educational future is about to change. However, if our results are correct and not over fit to the University of Central Florida and our theoretical speculations have some validity, the future of blended learning should encourage us about the coming changes.

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The authors acknowledge the contributions of several investigators and course developers from the Center for Distributed Learning at the University of Central Florida, the McKay School of Education at Brigham Young University, and Scholars at Umea University, Sweden. These professionals contributed theoretical and practical ideas to this research project and carefully reviewed earlier versions of this manuscript. The Authors gratefully acknowledge their support and assistance.

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Dziuban, C., Graham, C.R., Moskal, P.D. et al. Blended learning: the new normal and emerging technologies. Int J Educ Technol High Educ 15 , 3 (2018). https://doi.org/10.1186/s41239-017-0087-5

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The “new normal” in education

José augusto pacheco.

Research Centre on Education (CIEd), Institute of Education, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal

Effects rippling from the Covid 19 emergency include changes in the personal, social, and economic spheres. Are there continuities as well? Based on a literature review (primarily of UNESCO and OECD publications and their critics), the following question is posed: How can one resist the slide into passive technologization and seize the possibility of achieving a responsive, ethical, humane, and international-transformational approach to education? Technologization, while an ongoing and evidently ever-intensifying tendency, is not without its critics, especially those associated with the humanistic tradition in education. This is more apparent now that curriculum is being conceived as a complicated conversation. In a complex and unequal world, the well-being of students requires diverse and even conflicting visions of the world, its problems, and the forms of knowledge we study to address them.

From the past, we might find our way to a future unforeclosed by the present (Pinar 2019 , p. 12)

Texts regarding this pandemic’s consequences are appearing at an accelerating pace, with constant coverage by news outlets, as well as philosophical, historical, and sociological reflections by public intellectuals worldwide. Ripples from the current emergency have spread into the personal, social, and economic spheres. But are there continuities as well? Is the pandemic creating a “new normal” in education or simply accenting what has already become normal—an accelerating tendency toward technologization? This tendency presents an important challenge for education, requiring a critical vision of post-Covid-19 curriculum. One must pose an additional question: How can one resist the slide into passive technologization and seize the possibility of achieving a responsive, ethical, humane, and international-transformational approach to education?

The ongoing present

Unpredicted except through science fiction, movie scripts, and novels, the Covid-19 pandemic has changed everyday life, caused wide-scale illness and death, and provoked preventive measures like social distancing, confinement, and school closures. It has struck disproportionately at those who provide essential services and those unable to work remotely; in an already precarious marketplace, unemployment is having terrible consequences. The pandemic is now the chief sign of both globalization and deglobalization, as nations close borders and airports sit empty. There are no departures, no delays. Everything has changed, and no one was prepared. The pandemic has disrupted the flow of time and unraveled what was normal. It is the emergence of an event (think of Badiou 2009 ) that restarts time, creates radical ruptures and imbalances, and brings about a contingency that becomes a new necessity (Žižek 2020 ). Such events question the ongoing present.

The pandemic has reshuffled our needs, which are now based on a new order. Whether of short or medium duration, will it end in a return to the “normal” or move us into an unknown future? Žižek contends that “there is no return to normal, the new ‘normal’ will have to be constructed on the ruins of our old lives, or we will find ourselves in a new barbarism whose signs are already clearly discernible” (Žižek 2020 , p. 3).

Despite public health measures, Gil ( 2020 ) observes that the pandemic has so far generated no physical or spiritual upheaval and no universal awareness of the need to change how we live. Techno-capitalism continues to work, though perhaps not as before. Online sales increase and professionals work from home, thereby creating new digital subjectivities and economies. We will not escape the pull of self-preservation, self-regeneration, and the metamorphosis of capitalism, which will continue its permanent revolution (Wells 2020 ). In adapting subjectivities to the recent demands of digital capitalism, the pandemic can catapult us into an even more thoroughly digitalized space, a trend that artificial intelligence will accelerate. These new subjectivities will exhibit increased capacities for voluntary obedience and programmable functioning abilities, leading to a “new normal” benefiting those who are savvy in software-structured social relationships.

The Covid-19 pandemic has submerged us all in the tsunami-like economies of the Cloud. There is an intensification of the allegro rhythm of adaptation to the Internet of Things (Davies, Beauchamp, Davies, and Price 2019 ). For Latour ( 2020 ), the pandemic has become internalized as an ongoing state of emergency preparing us for the next crisis—climate change—for which we will see just how (un)prepared we are. Along with inequality, climate is one of the most pressing issues of our time (OECD 2019a , 2019b ) and therefore its representation in the curriculum is of public, not just private, interest.

Education both reflects what is now and anticipates what is next, recoding private and public responses to crises. Žižek ( 2020 , p. 117) suggests in this regard that “values and beliefs should not be simply ignored: they play an important role and should be treated as a specific mode of assemblage”. As such, education is (post)human and has its (over)determination by beliefs and values, themselves encoded in technology.

Will the pandemic detoxify our addiction to technology, or will it cement that addiction? Pinar ( 2019 , pp. 14–15) suggests that “this idea—that technological advance can overcome cultural, economic, educational crises—has faded into the background. It is our assumption. Our faith prompts the purchase of new technology and assures we can cure climate change”. While waiting for technology to rescue us, we might also remember to look at ourselves. In this way, the pandemic could be a starting point for a more sustainable environment. An intelligent response to climate change, reactivating the humanistic tradition in education, would reaffirm the right to such an education as a global common good (UNESCO 2015a , p. 10):

This approach emphasizes the inclusion of people who are often subject to discrimination – women and girls, indigenous people, persons with disabilities, migrants, the elderly and people living in countries affected by conflict. It requires an open and flexible approach to learning that is both lifelong and life-wide: an approach that provides the opportunity for all to realize their potential for a sustainable future and a life of dignity”.

Pinar ( 2004 , 2009 , 2019 ) concevies of curriculum as a complicated conversation. Central to that complicated conversation is climate change, which drives the need for education for sustainable development and the grooming of new global citizens with sustainable lifestyles and exemplary environmental custodianship (Marope 2017 ).

The new normal

The pandemic ushers in a “new” normal, in which digitization enforces ways of working and learning. It forces education further into technologization, a development already well underway, fueled by commercialism and the reigning market ideology. Daniel ( 2020 , p. 1) notes that “many institutions had plans to make greater use of technology in teaching, but the outbreak of Covid-19 has meant that changes intended to occur over months or years had to be implemented in a few days”.

Is this “new normal” really new or is it a reiteration of the old?

Digital technologies are the visible face of the immediate changes taking place in society—the commercial society—and schools. The immediate solution to the closure of schools is distance learning, with platforms proliferating and knowledge demoted to information to be exchanged (Koopman 2019 ), like a product, a phenomenon predicted decades ago by Lyotard ( 1984 , pp. 4-5):

Knowledge is and will be produced in order to be sold, it is and will be consumed in order to be valued in a new production: in both cases, the goal is exchange. Knowledge ceases to be an end in itself, it loses its use-value.

Digital technologies and economic rationality based on performance are significant determinants of the commercialization of learning. Moving from physical face-to-face presence to virtual contact (synchronous and asynchronous), the learning space becomes disembodied, virtual not actual, impacting both student learning and the organization of schools, which are no longer buildings but websites. Such change is not only coterminous with the pandemic, as the Education 2030 Agenda (UNESCO 2015b ) testified; preceding that was the Delors Report (Delors 1996 ), which recoded education as lifelong learning that included learning to know, learning to do, learning to be, and learning to live together.

Transnational organizations have specified competences for the 21st century and, in the process, have defined disciplinary and interdisciplinary knowledge that encourages global citizenship, through “the supra curriculum at the global, regional, or international comparative level” (Marope 2017 , p. 10). According to UNESCO ( 2017 ):

While the world may be increasingly interconnected, human rights violations, inequality and poverty still threaten peace and sustainability. Global Citizenship Education (GCED) is UNESCO’s response to these challenges. It works by empowering learners of all ages to understand that these are global, not local issues and to become active promoters of more peaceful, tolerant, inclusive, secure and sustainable societies.

These transnational initiatives have not only acknowledged traditional school subjects but have also shifted the curriculum toward timely topics dedicated to understanding the emergencies of the day (Spiller 2017 ). However, for the OECD ( 2019a ), the “new normal” accentuates two ideas: competence-based education, which includes the knowledges identified in the Delors Report , and a new learning framework structured by digital technologies. The Covid-19 pandemic does not change this logic. Indeed, the interdisciplinary skills framework, content and standardized testing associated with the Programme for International Student Assessment of the OECD has become the most powerful tool for prescribing the curriculum. Educationally, “the universal homogenous ‘state’ exists already. Globalization of standardized testing—the most prominent instance of threatening to restructure schools into technological sites of political socialization, conditioning children for compliance to a universal homogeneous state of mind” (Pinar 2019 , p. 2).

In addition to cognitive and practical skills, this “homogenous state of mind” rests on so-called social and emotional skills in the service of learning to live together, affirming global citizenship, and presumably returning agency to students and teachers (OECD 2019a ). According to Marope ( 2017 , p. 22), “this calls for higher flexibility in curriculum development, and for the need to leave space for curricula interpretation, contextualization, and creativity at the micro level of teachers and classrooms”. Heterogeneity is thus enlisted in the service of both economic homogeneity and disciplinary knowledge. Disciplinary knowledge is presented as universal and endowed with social, moral, and cognitive authority. Operational and effective knowledge becomes central, due to the influence of financial lobbies, thereby ensuring that the logic of the market is brought into the practices of schools. As Pestre ( 2013 , p. 21) observed, “the nature of this knowledge is new: what matters is that it makes hic et nunc the action, its effect and not its understanding”. Its functionality follows (presumably) data and evidence-based management.

A new language is thus imposed on education and the curriculum. Such enforced installation of performative language and Big Data lead to effective and profitable operations in a vast market concerned with competence in operational skills (Lyotard 1984 ). This “new normal” curriculum is said to be more horizontal and less hierarchical and radically polycentric with problem-solving produced through social networks, NGOs, transnational organizations, and think tanks (Pestre 2013 ; Williamson 2013 , 2017 ). Untouched by the pandemic, the “new (old) normal” remains based on disciplinary knowledge and enmeshed in the discourse of standards and accountability in education.

Such enforced commercialism reflects and reinforces economic globalization. Pinar ( 2011 , p. 30) worries that “the globalization of instrumental rationality in education threatens the very existence of education itself”. In his theory, commercialism and the technical instrumentality by which homogenization advances erase education as an embodied experience and the curriculum as a humanistic project. It is a time in which the humanities are devalued as well, as acknowledged by Pinar ( 2019 , p. 19): “In the United States [and in the world] not only does economics replace education—STEM replace the liberal arts as central to the curriculum—there are even politicians who attack the liberal arts as subversive and irrelevant…it can be more precisely characterized as reckless rhetoric of a know-nothing populism”. Replacing in-person dialogical encounters and the educational cultivation of the person (via Bildung and currere ), digital technologies are creating uniformity of learning spaces, in spite of their individualistic tendencies. Of course, education occurs outside schools—and on occasion in schools—but this causal displacement of the centrality of the school implies a devaluation of academic knowledge in the name of diversification of learning spaces.

In society, education, and specifically in the curriculum, the pandemic has brought nothing new but rather has accelerated already existing trends that can be summarized as technologization. Those who can work “remotely” exercise their privilege, since they can exploit an increasingly digital society. They themselves are changed in the process, as their own subjectivities are digitalized, thus predisposing them to a “curriculum of things” (a term coined by Laist ( 2016 ) to describe an object-oriented pedagogical approach), which is organized not around knowledge but information (Koopman 2019 ; Couldry and Mejias 2019 ). This (old) “new normal” was advanced by the OECD, among other international organizations, thus precipitating what some see as “a dynamic and transformative articulation of collective expectations of the purpose, quality, and relevance of education and learning to holistic, inclusive, just, peaceful, and sustainable development, and to the well-being and fulfilment of current and future generations” (Marope 2017 , p. 13). Covid-19, illiberal democracy, economic nationalism, and inaction on climate change, all upend this promise.

Understanding the psychological and cultural complexity of the curriculum is crucial. Without appreciating the infinity of responses students have to what they study, one cannot engage in the complicated conversation that is the curriculum. There must be an affirmation of “not only the individualism of a person’s experience but [of what is] underlining the significance of a person’s response to a course of study that has been designed to ignore individuality in order to buttress nation, religion, ethnicity, family, and gender” (Grumet 2017 , p. 77). Rather than promoting neuroscience as the answer to the problems of curriculum and pedagogy, it is long-past time for rethinking curriculum development and addressing the canonical curriculum question: What knowledge is of most worth from a humanistic perspective that is structured by complicated conversation (UNESCO 2015a ; Pinar 2004 , 2019 )? It promotes respect for diversity and rejection of all forms of (cultural) hegemony, stereotypes, and biases (Pacheco 2009 , 2017 ).

Revisiting the curriculum in the Covid-19 era then expresses the fallacy of the “new normal” but also represents a particular opportunity to promote a different path forward.

Looking to the post-Covid-19 curriculum

Based on the notion of curriculum as a complicated conversation, as proposed by Pinar ( 2004 ), the post-Covid-19 curriculum can seize the possibility of achieving a responsive, ethical, humane education, one which requires a humanistic and internationally aware reconceptualization of curriculum.

While beliefs and values are anchored in social and individual practices (Pinar 2019 , p. 15), education extracts them for critique and reconsideration. For example, freedom and tolerance are not neutral but normative practices, however ideology-free policymakers imagine them to be.

That same sleight-of-hand—value neutrality in the service of a certain normativity—is evident in a digital concept of society as a relationship between humans and non-humans (or posthumans), a relationship not only mediated by but encapsulated within technology: machines interfacing with other machines. This is not merely a technological change, as if it were a quarantined domain severed from society. Technologization is a totalizing digitalization of human experience that includes the structures of society. It is less social than economic, with social bonds now recoded as financial transactions sutured by software. Now that subjectivity is digitalized, the human face has become an exclusively economic one that fabricates the fantasy of rational and free agents—always self-interested—operating in supposedly free markets. Oddly enough, there is no place for a vision of humanistic and internationally aware change. The technological dimension of curriculum is assumed to be the primary area of change, which has been deeply and totally imposed by global standards. The worldwide pandemic supports arguments for imposing forms of control (Žižek 2020 ), including the geolocation of infected people and the suspension—in a state of exception—of civil liberties.

By destroying democracy, the technology of control leads to totalitarianism and barbarism, ending tolerance, difference, and diversity. Remembrance and memory are needed so that historical fascisms (Eley 2020 ) are not repeated, albeit in new disguises (Adorno 2011 ). Technologized education enhances efficiency and ensures uniformity, while presuming objectivity to the detriment of human reflection and singularity. It imposes the running data of the Curriculum of Things and eschews intellectual endeavor, critical attitude, and self-reflexivity.

For those who advocate the primacy of technology and the so-called “free market”, the pandemic represents opportunities not only for profit but also for confirmation of the pervasiveness of human error and proof of the efficiency of the non-human, i.e., the inhuman technology. What may possibly protect children from this inhumanity and their commodification, as human capital, is a humane or humanistic education that contradicts their commodification.

The decontextualized technical vocabulary in use in a market society produces an undifferentiated image in which people are blinded to nuance, distinction, and subtlety. For Pestre, concepts associated with efficiency convey the primacy of economic activity to the exclusion, for instance, of ethics, since those concepts devalue historic (if unrealized) commitments to equality and fraternity by instead emphasizing economic freedom and the autonomy of self-interested individuals. Constructing education as solely economic and technological constitutes a movement toward total efficiency through the installation of uniformity of behavior, devaluing diversity and human creativity.

Erased from the screen is any image of public education as a space of freedom, or as Macdonald ( 1995 , p. 38) holds, any image or concept of “the dignity and integrity of each human”. Instead, what we face is the post-human and the undisputed reign of instrumental reality, where the ends justify the means and human realization is reduced to the consumption of goods and experiences. As Pinar ( 2019 , p. 7) observes: “In the private sphere…. freedom is recast as a choice of consumer goods; in the public sphere, it converts to control and the demand that freedom flourish, so that whatever is profitable can be pursued”. Such “negative” freedom—freedom from constraint—ignores “positive” freedom, which requires us to contemplate—in ethical and spiritual terms—what that freedom is for. To contemplate what freedom is for requires “critical and comprehensive knowledge” (Pestre 2013 , p. 39) not only instrumental and technical knowledge. The humanities and the arts would reoccupy the center of such a curriculum and not be related to its margins (Westbury 2008 ), acknowledging that what is studied within schools is a complicated conversation among those present—including oneself, one’s ancestors, and those yet to be born (Pinar 2004 ).

In an era of unconstrained technologization, the challenge facing the curriculum is coding and STEM (science, technology, engineering, and mathematics), with technology dislodging those subjects related to the human. This is not a classical curriculum (although it could be) but one focused on the emergencies of the moment–namely, climate change, the pandemic, mass migration, right-wing populism, and economic inequality. These timely topics, which in secondary school could be taught as short courses and at the elementary level as thematic units, would be informed by the traditional school subjects (yes, including STEM). Such a reorganization of the curriculum would allow students to see how academic knowledge enables them to understand what is happening to them and their parents in their own regions and globally. Such a cosmopolitan curriculum would prepare children to become citizens not only of their own nations but of the world. This citizenship would simultaneously be subjective and social, singular and universal (Marope 2020 ). Pinar ( 2019 , p. 5) reminds us that “the division between private and public was first blurred then erased by technology”:

No longer public, let alone sacred, morality becomes a matter of privately held values, sometimes monetized as commodities, statements of personal preference, often ornamental, sometimes self-servingly instrumental. Whatever their function, values were to be confined to the private sphere. The public sphere was no longer the civic square but rather, the marketplace, the site where one purchased whatever one valued.

New technological spaces are the universal center for (in)human values. The civic square is now Amazon, Alibaba, Twitter, WeChat, and other global online corporations. The facts of our human condition—a century-old phrase uncanny in its echoes today—can be studied in schools as an interdisciplinary complicated conversation about public issues that eclipse private ones (Pinar 2019 ), including social injustice, inequality, democracy, climate change, refugees, immigrants, and minority groups. Understood as a responsive, ethical, humane and transformational international educational approach, such a post-Covid-19 curriculum could be a “force for social equity, justice, cohesion, stability, and peace” (Marope 2017 , p. 32). “Unchosen” is certainly the adjective describing our obligations now, as we are surrounded by death and dying and threatened by privation or even starvation, as economies collapse and food-supply chains are broken. The pandemic may not mean deglobalization, but it surely accentuates it, as national borders are closed, international travel is suspended, and international trade is impacted by the accompanying economic crisis. On the other hand, economic globalization could return even stronger, as could the globalization of education systems. The “new normal” in education is the technological order—a passive technologization—and its expansion continues uncontested and even accelerated by the pandemic.

Two Greek concepts, kronos and kairos , allow a discussion of contrasts between the quantitative and the qualitative in education. Echoing the ancient notion of kronos are the technologically structured curriculum values of quantity and performance, which are always assessed by a standardized accountability system enforcing an “ideology of achievement”. “While kronos refers to chronological or sequential time, kairos refers to time that might require waiting patiently for a long time or immediate and rapid action; which course of action one chooses will depend on the particular situation” (Lahtinen 2009 , p. 252).

For Macdonald ( 1995 , p. 51), “the central ideology of the schools is the ideology of achievement …[It] is a quantitative ideology, for even to attempt to assess quality must be quantified under this ideology, and the educational process is perceived as a technically monitored quality control process”.

Self-evaluation subjectively internalizes what is useful and in conformity with the techno-economy and its so-called standards, increasingly enforcing technical (software) forms. If recoded as the Internet of Things, this remains a curriculum in allegiance with “order and control” (Doll 2013 , p. 314) School knowledge is reduced to an instrument for economic success, employing compulsory collaboration to ensure group think and conformity. Intertwined with the Internet of Things, technological subjectivity becomes embedded in software, redesigned for effectiveness, i.e., or use-value (as Lyotard predicted).

The Curriculum of Things dominates the Internet, which is simultaneously an object and a thing (see Heidegger 1967 , 1971 , 1977 ), a powerful “technological tool for the process of knowledge building” (Means 2008 , p. 137). Online learning occupies the subjective zone between the “curriculum-as-planned” and the “curriculum-as-lived” (Pinar 2019 , p. 23). The world of the curriculum-as-lived fades, as the screen shifts and children are enmeshed in an ocularcentric system of accountability and instrumentality.

In contrast to kronos , the Greek concept of kairos implies lived time or even slow time (Koepnick 2014 ), time that is “self-reflective” (Macdonald 1995 , p. 103) and autobiographical (Pinar 2009 , 2004), thus inspiring “curriculum improvisation” (Aoki 2011 , p. 375), while emphasizing “the plurality of subjectivities” (Grumet 2017 , p. 80). Kairos emphasizes singularity and acknowledges particularities; it is skeptical of similarities. For Shew ( 2013 , p. 48), “ kairos is that which opens an originary experience—of the divine, perhaps, but also of life or being. Thought as such, kairos as a formative happening—an opportune moment, crisis, circumstance, event—imposes its own sense of measure on time”. So conceived, curriculum can become a complicated conversation that occurs not in chronological time but in its own time. Such dialogue is not neutral, apolitical, or timeless. It focuses on the present and is intrinsically subjective, even in public space, as Pinar ( 2019 , p. 12) writes: “its site is subjectivity as one attunes oneself to what one is experiencing, yes to its immediacy and specificity but also to its situatedness, relatedness, including to what lies beyond it and not only spatially but temporally”.

Kairos is, then, the uniqueness of time that converts curriculum into a complicated conversation, one that includes the subjective reconstruction of learning as a consciousness of everyday life, encouraging the inner activism of quietude and disquietude. Writing about eternity, as an orientation towards the future, Pinar ( 2019 , p. 2) argues that “the second side [the first is contemplation] of such consciousness is immersion in daily life, the activism of quietude – for example, ethical engagement with others”. We add disquietude now, following the work of the Portuguese poet Fernando Pessoa. Disquietude is a moment of eternity: “Sometimes I think I’ll never leave ‘Douradores’ Street. And having written this, it seems to me eternity. Neither pleasure, nor glory, nor power. Freedom, only freedom” (Pesssoa 1991 ).

The disquietude conversation is simultaneously individual and public. It establishes an international space both deglobalized and autonomous, a source of responsive, ethical, and humane encounter. No longer entranced by the distracting dynamic stasis of image-after-image on the screen, the student can face what is his or her emplacement in the physical and natural world, as well as the technological world. The student can become present as a person, here and now, simultaneously historical and timeless.

Conclusions

Slow down and linger should be our motto now. A slogan yes, but it also represents a political, as well as a psychological resistance to the acceleration of time (Berg and Seeber 2016 )—an acceleration that the pandemic has intensified. Covid-19 has moved curriculum online, forcing children physically apart from each other and from their teachers and especially from the in-person dialogical encounters that classrooms can provide. The public space disappears into the pre-designed screen space that software allows, and the machine now becomes the material basis for a curriculum of things, not persons. Like the virus, the pandemic curriculum becomes embedded in devices that technologize our children.

Although one hundred years old, the images created in Modern Times by Charlie Chaplin return, less humorous this time than emblematic of our intensifying subjection to technological necessity. It “would seem to leave us as cogs in the machine, ourselves like moving parts, we keep functioning efficiently, increasing productivity calculating the creative destruction of what is, the human now materialized (de)vices ensnaring us in convenience, connectivity, calculation” (Pinar 2019 , p. 9). Post-human, as many would say.

Technology supports standardized testing and enforces software-designed conformity and never-ending self-evaluation, while all the time erasing lived, embodied experience and intellectual independence. Ignoring the evidence, others are sure that technology can function differently: “Given the potential of information and communication technologies, the teacher should now be a guide who enables learners, from early childhood throughout their learning trajectories, to develop and advance through the constantly expanding maze of knowledge” (UNESCO 2015a , p. 51). Would that it were so.

The canonical question—What knowledge is of most worth?—is open-ended and contentious. In a technologized world, providing for the well-being of children is not obvious, as well-being is embedded in ancient, non-neoliberal visions of the world. “Education is everybody’s business”, Pinar ( 2019 , p. 2) points out, as it fosters “responsible citizenship and solidarity in a global world” (UNESCO 2015a , p. 66), resisting inequality and the exclusion, for example, of migrant groups, refugees, and even those who live below or on the edge of poverty.

In this fast-moving digital world, education needs to be inclusive but not conformist. As the United Nations ( 2015 ) declares, education should ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. “The coming years will be a vital period to save the planet and to achieve sustainable, inclusive human development” (United Nations 2019 , p. 64). Is such sustainable, inclusive human development achievable through technologization? Can technology succeed where religion has failed?

Despite its contradictions and economic emphases, public education has one clear obligation—to create embodied encounters of learning through curriculum conceived as a complicated conversation. Such a conception acknowledges the worldliness of a cosmopolitan curriculum as it affirms the personification of the individual (Pinar 2011 ). As noted by Grumet ( 2017 , p. 89), “as a form of ethics, there is a responsibility to participate in conversation”. Certainly, it is necessary to ask over and over again the canonical curriculum question: What knowledge is of most worth?

If time, technology and teaching are moving images of eternity, curriculum and pedagogy are also, both ‘moving’ and ‘images’ but not an explicit, empirical, or exact representation of eternity…if reality is an endless series of ‘moving images’, the canonical curriculum question—What knowledge is of most worth?—cannot be settled for all time by declaring one set of subjects eternally important” (Pinar 2019 , p. 12).

In a complicated conversation, the curriculum is not a fixed image sliding into a passive technologization. As a “moving image”, the curriculum constitutes a politics of presence, an ongoing expression of subjectivity (Grumet 2017 ) that affirms the infinity of reality: “Shifting one’s attitude from ‘reducing’ complexity to ‘embracing’ what is always already present in relations and interactions may lead to thinking complexly, abiding happily with mystery” (Doll 2012 , p. 172). Describing the dialogical encounter characterizing conceived curriculum, as a complicated conversation, Pinar explains that this moment of dialogue “is not only place-sensitive (perhaps classroom centered) but also within oneself”, because “the educational significance of subject matter is that it enables the student to learn from actual embodied experience, an outcome that cannot always be engineered” (Pinar 2019 , pp. 12–13). Lived experience is not technological. So, “the curriculum of the future is not just a matter of defining content and official knowledge. It is about creating, sculpting, and finessing minds, mentalities, and identities, promoting style of thought about humans, or ‘mashing up’ and ‘making up’ the future of people” (Williamson 2013 , p. 113).

Yes, we need to linger and take time to contemplate the curriculum question. Only in this way will we share what is common and distinctive in our experience of the current pandemic by changing our time and our learning to foreclose on our future. Curriculum conceived as a complicated conversation restarts historical not screen time; it enacts the private and public as distinguishable, not fused in a computer screen. That is the “new normal”.

is full professor in the Department of Curriculum Studies and Educational Technology (Institute of Education, University of Minho, Portugal). His research focuses on curriculum theory, curriculum politics, and teacher training and evaluation. Presently, he is director of the PhD Science Education Program of the University of Minho, member of the Advisory Board of the Organization of Ibero-American Studies, director of the European Journal of Curriculum Studies, and director of the European Association on Curriculum Studies.

My thanks to William F. Pinar. Friendship is another moving image of eternity. I am grateful to the anonymous reviewer. This work is financed by national funds through the FCT - Foundation for Science and Technology, under the project PTDC / CED-EDG / 30410/2017, Centre for Research in Education, Institute of Education, University of Minho.

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UNCOVERING LEARNERS’ EXPERIENCES TO NEW NORMAL EDUCATION: IMPLICATIONS OF ASYNCHRONOUS INSTRUCTION IN GE 5: SCIENCE, TECHNOLOGY, AND SOCIETY COURSE TEACHING

The new normal education policy in response to the pandemic crisis pushed institutions to shift from traditional face-to-face to asynchronous instruction that posed challenges particularly to science courses in higher education. The purpose of this study was to understand the learning experiences of the students and the implications of asynchronous teaching instruction in the Science, Technology, and Society course. This study utilized a convergent parallel mixed method of research employing descriptive-comparative and descriptive phenomenological research designs. There were 100 respondents for the quantitative part and 12 participants for the qualitative part. Based on the quantitative findings, the overall implementation of asynchronous instruction in the course was "excellent." Specifically, the level of implementation was "very satisfactory" in terms of Content and Course Evaluation, while "excellent" in terms of Instructional Design, Student Assessment, and Technology. There was no significant difference in the level of implementation of the course asynchronous instruction when analyzed by specialization. Moreover, based on the qualitative analysis, the learning experiences of students in asynchronous instruction were both positive and negative that implied two-way learning experiences. The general recommendation gleaned from the students was science, technology, and society asynchronous delivery improvement that covered teacher improvement, SIM improvement, and assessment tool improvement. The general recommendations of this study were improving asynchronous instruction delivery through teachers training proposals, modification of self-instructional materials, increasing the awareness and effective use of the varied assessment tools in sustaining the needs and interest of students in studying the course, creating a safe learning environment for the students, and conducting future researches to reveal significant factors which affect the learning experiences of students and the other points that the current researchers have not yet explored.

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Research Topics & Ideas: Education

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If you’re just starting out exploring education-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from actual dissertations and theses..

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Education Research Topics

• How to find a research topic (video)
• List of 50+ education-related research topics/ideas
• List of 120+ level-specific research topics 
• Examples of actual dissertation topics in education
• Tips to fast-track your topic ideation (video)
• Free Webinar : Topic Ideation 101
• Where to get extra help

Education-Related Research Topics & Ideas

Below you’ll find a list of education-related research topics and idea kickstarters. These are fairly broad and flexible to various contexts, so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• The impact of school funding on student achievement
• The effects of social and emotional learning on student well-being
• The effects of parental involvement on student behaviour
• The impact of teacher training on student learning
• The impact of classroom design on student learning
• The impact of poverty on education
• The use of student data to inform instruction
• The role of parental involvement in education
• The effects of mindfulness practices in the classroom
• The use of technology in the classroom
• The role of critical thinking in education
• The use of formative and summative assessments in the classroom
• The use of differentiated instruction in the classroom
• The use of gamification in education
• The effects of teacher burnout on student learning
• The impact of school leadership on student achievement
• The effects of teacher diversity on student outcomes
• The role of teacher collaboration in improving student outcomes
• The implementation of blended and online learning
• The effects of teacher accountability on student achievement
• The effects of standardized testing on student learning
• The effects of classroom management on student behaviour
• The effects of school culture on student achievement
• The use of student-centred learning in the classroom
• The impact of teacher-student relationships on student outcomes
• The achievement gap in minority and low-income students
• The use of culturally responsive teaching in the classroom
• The impact of teacher professional development on student learning
• The use of project-based learning in the classroom
• The effects of teacher expectations on student achievement
• The use of adaptive learning technology in the classroom
• The impact of teacher turnover on student learning
• The effects of teacher recruitment and retention on student learning
• The impact of early childhood education on later academic success
• The impact of parental involvement on student engagement
• The use of positive reinforcement in education
• The impact of school climate on student engagement
• The role of STEM education in preparing students for the workforce
• The effects of school choice on student achievement
• The use of technology in the form of online tutoring

Level-Specific Research Topics

Looking for research topics for a specific level of education? We’ve got you covered. Below you can find research topic ideas for primary, secondary and tertiary-level education contexts. Click the relevant level to view the respective list.

Research Topics: Pick An Education Level

Primary education.

• Investigating the effects of peer tutoring on academic achievement in primary school
• Exploring the benefits of mindfulness practices in primary school classrooms
• Examining the effects of different teaching strategies on primary school students’ problem-solving skills
• The use of storytelling as a teaching strategy in primary school literacy instruction
• The role of cultural diversity in promoting tolerance and understanding in primary schools
• The impact of character education programs on moral development in primary school students
• Investigating the use of technology in enhancing primary school mathematics education
• The impact of inclusive curriculum on promoting equity and diversity in primary schools
• The impact of outdoor education programs on environmental awareness in primary school students
• The influence of school climate on student motivation and engagement in primary schools
• Investigating the effects of early literacy interventions on reading comprehension in primary school students
• The impact of parental involvement in school decision-making processes on student achievement in primary schools
• Exploring the benefits of inclusive education for students with special needs in primary schools
• Investigating the effects of teacher-student feedback on academic motivation in primary schools
• The role of technology in developing digital literacy skills in primary school students
• Effective strategies for fostering a growth mindset in primary school students
• Investigating the role of parental support in reducing academic stress in primary school children
• The role of arts education in fostering creativity and self-expression in primary school students
• Examining the effects of early childhood education programs on primary school readiness
• Examining the effects of homework on primary school students’ academic performance
• The role of formative assessment in improving learning outcomes in primary school classrooms
• The impact of teacher-student relationships on academic outcomes in primary school
• Investigating the effects of classroom environment on student behavior and learning outcomes in primary schools
• Investigating the role of creativity and imagination in primary school curriculum
• The impact of nutrition and healthy eating programs on academic performance in primary schools
• The impact of social-emotional learning programs on primary school students’ well-being and academic performance
• The role of parental involvement in academic achievement of primary school children
• Examining the effects of classroom management strategies on student behavior in primary school
• The role of school leadership in creating a positive school climate Exploring the benefits of bilingual education in primary schools
• The effectiveness of project-based learning in developing critical thinking skills in primary school students
• The role of inquiry-based learning in fostering curiosity and critical thinking in primary school students
• The effects of class size on student engagement and achievement in primary schools
• Investigating the effects of recess and physical activity breaks on attention and learning in primary school
• Exploring the benefits of outdoor play in developing gross motor skills in primary school children
• The effects of educational field trips on knowledge retention in primary school students
• Examining the effects of inclusive classroom practices on students’ attitudes towards diversity in primary schools
• The impact of parental involvement in homework on primary school students’ academic achievement
• Investigating the effectiveness of different assessment methods in primary school classrooms
• The influence of physical activity and exercise on cognitive development in primary school children
• Exploring the benefits of cooperative learning in promoting social skills in primary school students

Secondary Education

• Investigating the effects of school discipline policies on student behavior and academic success in secondary education
• The role of social media in enhancing communication and collaboration among secondary school students
• The impact of school leadership on teacher effectiveness and student outcomes in secondary schools
• Investigating the effects of technology integration on teaching and learning in secondary education
• Exploring the benefits of interdisciplinary instruction in promoting critical thinking skills in secondary schools
• The impact of arts education on creativity and self-expression in secondary school students
• The effectiveness of flipped classrooms in promoting student learning in secondary education
• The role of career guidance programs in preparing secondary school students for future employment
• Investigating the effects of student-centered learning approaches on student autonomy and academic success in secondary schools
• The impact of socio-economic factors on educational attainment in secondary education
• Investigating the impact of project-based learning on student engagement and academic achievement in secondary schools
• Investigating the effects of multicultural education on cultural understanding and tolerance in secondary schools
• The influence of standardized testing on teaching practices and student learning in secondary education
• Investigating the effects of classroom management strategies on student behavior and academic engagement in secondary education
• The influence of teacher professional development on instructional practices and student outcomes in secondary schools
• The role of extracurricular activities in promoting holistic development and well-roundedness in secondary school students
• Investigating the effects of blended learning models on student engagement and achievement in secondary education
• The role of physical education in promoting physical health and well-being among secondary school students
• Investigating the effects of gender on academic achievement and career aspirations in secondary education
• Exploring the benefits of multicultural literature in promoting cultural awareness and empathy among secondary school students
• The impact of school counseling services on student mental health and well-being in secondary schools
• Exploring the benefits of vocational education and training in preparing secondary school students for the workforce
• The role of digital literacy in preparing secondary school students for the digital age
• The influence of parental involvement on academic success and well-being of secondary school students
• The impact of social-emotional learning programs on secondary school students’ well-being and academic success
• The role of character education in fostering ethical and responsible behavior in secondary school students
• Examining the effects of digital citizenship education on responsible and ethical technology use among secondary school students
• The impact of parental involvement in school decision-making processes on student outcomes in secondary schools
• The role of educational technology in promoting personalized learning experiences in secondary schools
• The impact of inclusive education on the social and academic outcomes of students with disabilities in secondary schools
• The influence of parental support on academic motivation and achievement in secondary education
• The role of school climate in promoting positive behavior and well-being among secondary school students
• Examining the effects of peer mentoring programs on academic achievement and social-emotional development in secondary schools
• Examining the effects of teacher-student relationships on student motivation and achievement in secondary schools
• Exploring the benefits of service-learning programs in promoting civic engagement among secondary school students
• The impact of educational policies on educational equity and access in secondary education
• Examining the effects of homework on academic achievement and student well-being in secondary education
• Investigating the effects of different assessment methods on student performance in secondary schools
• Examining the effects of single-sex education on academic performance and gender stereotypes in secondary schools
• The role of mentoring programs in supporting the transition from secondary to post-secondary education

Tertiary Education

• The role of student support services in promoting academic success and well-being in higher education
• The impact of internationalization initiatives on students’ intercultural competence and global perspectives in tertiary education
• Investigating the effects of active learning classrooms and learning spaces on student engagement and learning outcomes in tertiary education
• Exploring the benefits of service-learning experiences in fostering civic engagement and social responsibility in higher education
• The influence of learning communities and collaborative learning environments on student academic and social integration in higher education
• Exploring the benefits of undergraduate research experiences in fostering critical thinking and scientific inquiry skills
• Investigating the effects of academic advising and mentoring on student retention and degree completion in higher education
• The role of student engagement and involvement in co-curricular activities on holistic student development in higher education
• The impact of multicultural education on fostering cultural competence and diversity appreciation in higher education
• The role of internships and work-integrated learning experiences in enhancing students’ employability and career outcomes
• Examining the effects of assessment and feedback practices on student learning and academic achievement in tertiary education
• The influence of faculty professional development on instructional practices and student outcomes in tertiary education
• The influence of faculty-student relationships on student success and well-being in tertiary education
• The impact of college transition programs on students’ academic and social adjustment to higher education
• The impact of online learning platforms on student learning outcomes in higher education
• The impact of financial aid and scholarships on access and persistence in higher education
• The influence of student leadership and involvement in extracurricular activities on personal development and campus engagement
• Exploring the benefits of competency-based education in developing job-specific skills in tertiary students
• Examining the effects of flipped classroom models on student learning and retention in higher education
• Exploring the benefits of online collaboration and virtual team projects in developing teamwork skills in tertiary students
• Investigating the effects of diversity and inclusion initiatives on campus climate and student experiences in tertiary education
• The influence of study abroad programs on intercultural competence and global perspectives of college students
• Investigating the effects of peer mentoring and tutoring programs on student retention and academic performance in tertiary education
• Investigating the effectiveness of active learning strategies in promoting student engagement and achievement in tertiary education
• Investigating the effects of blended learning models and hybrid courses on student learning and satisfaction in higher education
• The role of digital literacy and information literacy skills in supporting student success in the digital age
• Investigating the effects of experiential learning opportunities on career readiness and employability of college students
• The impact of e-portfolios on student reflection, self-assessment, and showcasing of learning in higher education
• The role of technology in enhancing collaborative learning experiences in tertiary classrooms
• The impact of research opportunities on undergraduate student engagement and pursuit of advanced degrees
• Examining the effects of competency-based assessment on measuring student learning and achievement in tertiary education
• Examining the effects of interdisciplinary programs and courses on critical thinking and problem-solving skills in college students
• The role of inclusive education and accessibility in promoting equitable learning experiences for diverse student populations
• The role of career counseling and guidance in supporting students’ career decision-making in tertiary education
• The influence of faculty diversity and representation on student success and inclusive learning environments in higher education

Education-Related Dissertations & Theses

While the ideas we’ve presented above are a decent starting point for finding a research topic in education, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses in the education space to see how this all comes together in practice.

Below, we’ve included a selection of education-related research projects to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• From Rural to Urban: Education Conditions of Migrant Children in China (Wang, 2019)
• Energy Renovation While Learning English: A Guidebook for Elementary ESL Teachers (Yang, 2019)
• A Reanalyses of Intercorrelational Matrices of Visual and Verbal Learners’ Abilities, Cognitive Styles, and Learning Preferences (Fox, 2020)
• A study of the elementary math program utilized by a mid-Missouri school district (Barabas, 2020)
• Instructor formative assessment practices in virtual learning environments : a posthumanist sociomaterial perspective (Burcks, 2019)
• Higher education students services: a qualitative study of two mid-size universities’ direct exchange programs (Kinde, 2020)
• Exploring editorial leadership : a qualitative study of scholastic journalism advisers teaching leadership in Missouri secondary schools (Lewis, 2020)
• Selling the virtual university: a multimodal discourse analysis of marketing for online learning (Ludwig, 2020)
• Advocacy and accountability in school counselling: assessing the use of data as related to professional self-efficacy (Matthews, 2020)
• The use of an application screening assessment as a predictor of teaching retention at a midwestern, K-12, public school district (Scarbrough, 2020)
• Core values driving sustained elite performance cultures (Beiner, 2020)
• Educative features of upper elementary Eureka math curriculum (Dwiggins, 2020)
• How female principals nurture adult learning opportunities in successful high schools with challenging student demographics (Woodward, 2020)
• The disproportionality of Black Males in Special Education: A Case Study Analysis of Educator Perceptions in a Southeastern Urban High School (McCrae, 2021)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic within education, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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COVID-19 and the Educational Response: New Educational and Social Realities

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This research topic inquires into multiple and diverse impacts of the Covid-19 pandemic on education within various international contexts as billions navigate new educational and social realities. This crisis has led educators at all levels of PreK-20 and their stakeholders to question basic ...

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Program Title:

Advanced Computing Systems & Services: Adapting to the Rapid Evolution of Science and Engineering Research 2.0

The intent of this solicitation is to request proposals from organizations who are willing to serve as resource providers within the NSF Advanced Computing Systems and Services (ACSS) program. Resource providers would (1) provide advanced cyberinfrastructure (CI) resources in production operations to support the full range of computation, data-analysis, and AI research across all of science and engineering (S&E), and (2) enable democratized and equitable access to the proposed resources. The current solicitation is intended to complement previous NSF investments in advanced computational infrastructure by provisioning resources, broadly defined in this solicitation to include systems and services, in two categories: Category I, Capacity Resources: production computational resources maximizing the capacity provided to support the broad range of computation, data analytics and AI needs in S&E research; and Category II, Innovative Prototypes/Testbeds: innovative forward-looking capabilities deploying novel technologies, architectures, usage modes, etc., and exploring new target applications, methods, and paradigms. Resource Providers supported via this solicitation will be incorporated into NSF’s ACSS 2.0 program portfolio. This program complements investments in leadership-class computing and funds a federation of nationally available advanced computing resources that are technically diverse and intended to enable discoveries at a computational scale beyond the research of individual or regional academic institutions. NSF anticipates that at least 90% of the provisioned resource will be available to the S&E community through an open peer-reviewed national allocation process and have resource users be supported by community and other support services. Such allocation and support services are expected to be coordinated through the NSF-funded Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) , the National AI Research Resource , or an NSF-approved alternative as may emerge. Provisioning novel, diverse computational resources nationally at scale will require capability and capacity to support researchers who need assistance to use these resources. User support may be provided via various means, e.g., training sessions, documentation, direct engagement in response to tickets created via the ACCESS program, or integration of novel, NSF-funded software tools. The ACSS 2.0 Program seeks broad representation of PIs across the full spectrum of diverse community talent (including the participation of groups that have traditional been underrepresented in the cyberinfrastructure community) and institutions (including those that have not historically provided nationally allocatable cyberinfrastructure) in both the community of resource recipients and resources users to continue growing the scale and diversity of the S&E community. Submission from or partnership with EPSCoR institutions and institutions that have not previously received ACSS awards is encouraged.

Cognizant Program Officer(s):

Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact.

Robert Chadduck, Program Director, CISE/OAC, telephone: (703) 292-8970, email: [email protected]

Andrey Kanaev, Program Director, CISE/OAC, telephone: (703) 292-2841, email: [email protected]

Alejandro Suarez, Program Director, CISE/OAC, telephone: (703) 292-7092, email: [email protected]

Sharon Geva, telephone: (703) 292-7058, email: [email protected]

• 47.070 --- Computer and Information Science and Engineering

Award Information

Anticipated Type of Award: Cooperative Agreement

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds and the year of submission.

During submission cycles accepting Category I proposals, no Category II proposals will be considered.

Category I awards shall be between $10,000,000 and $20,000,000 for up to 5 years of duration.

During submission cycles accepting Category II proposals, no Category I proposals will be considered.

Category II awards shall not exceed a total of $5,000,000 and 5 years of duration.

Anticipated Funding Amount: $60,000,000

Estimated program budget and number of awards are subject to the availability of funds.

It is anticipated that, during submission cycles accepting Category I proposals, 1-3 Category I awards will be made at between $10,000,000 and $20,000,000 per award for up to five years. During submission cycles accepting Category II proposals, 1-4 Category II awards will be made at up to $5,000,000 per award for up to five years. User support and operating costs are expected to be up to 15% of the total resource acquisition cost per year for each deployed Category I or Category II system/service for up to five years; projected annual operating costs above 15% of the total resource acquisition cost must be discussed with a cognizant Program Officer prior to submission and will be considered in relation to the justified need. User support and operating costs will be funded via consideration of a supplement to the awarded cooperative agreement or other proposal mechanism. Proposals should provide an analysis of the projected annual operating costs of the proposed resource for a period of up to five years.

In Category I or Category II, there is a possibility of a renewal award contingent upon availability of funds and the successful evaluation of the resource provider’s performance as well as NSF merit review of the renewal proposal. During annual reviews, the Category I and Category II Resource Provider’s achievements and future plans will be comprehensively evaluated according to the criteria defined in the initial award, associated metrics, and other relevant criteria. Contingent on a successful third-year review, a Category I or Category II Resource Provider may be invited by NSF to submit a renewal proposal in the same Category as the original award, for up to five years commencing at the beginning of the fifth year of the original award.

Eligibility Information

Who May Submit Proposals:

Proposals may only be submitted by the following: Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus. Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. Other Federal Agencies and Federally Funded Research and Development Centers (FFRDCs): Contact the appropriate program before preparing a proposal for submission.

Who May Serve as PI:

There are no restrictions or limits.

Limit on Number of Proposals per Organization: 1

An organization may submit only one proposal per each competition specified in this solicitation but may be a subawardee on other proposals responding to this solicitation. The restriction to no more than one submitted proposal as lead institution is to help ensure that there is appropriate institutional commitment necessary for responsible oversight, by the potential recipient institution, of a national resource. Collaborative projects may only be submitted as a single proposal in which a single award is being requested (PAPPG Chapter II.E.3.a). The involvement of partner organizations should be supported through subawards administered by the submitting organization. These eligibility constraints will be strictly enforced in order to treat everyone fairly and consistently. In the event that an organization exceeds this limit, the proposal received within the limit will be accepted based on the earliest date and time of proposal submission (i.e., the first proposal received will be accepted and the remainder will be returned without review). No exceptions will be made.

Limit on Number of Proposals per PI or co-PI: 1

An individual may be the PI or co-PI on no more than one proposal per each competition specified in this solicitation. There is no limit on the number of proposals with which an individual may be associated in other capacities, such as senior/key personnel. These eligibility constraints will be strictly enforced in order to treat everyone fairly and consistently. In the event that an individual exceeds this limit, the proposal received within the limit will be accepted based on the earliest date and time of proposal submission (i.e., the first proposal received will be accepted and the remainder will be returned without review). No exceptions will be made.

Proposal Preparation and Submission Instructions

A. proposal preparation instructions.

• Letters of Intent: Not required
• Preliminary Proposal Submission: Not required

Full Proposals:

• Full Proposals submitted via Research.gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .
• Full Proposals submitted via Grants.gov: NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov guidelines apply (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ).

B. Budgetary Information

Cost Sharing Requirements:

Inclusion of voluntary committed cost sharing is prohibited.

Indirect Cost (F&A) Limitations:

Not Applicable

Other Budgetary Limitations:

C. Due Dates

Proposal review information criteria.

Merit Review Criteria:

National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information

Award Conditions:

Standard NSF award conditions apply.

Reporting Requirements:

Additional reporting requirements apply. Please see the full text of this solicitation for further information.

I. Introduction

Today’s research discoveries at the confluence of theoretical, experimental, and computational S&E are enabled by the continuing availability of an ecosystem of advanced computational resources. For several decades, NSF has effectively supported the broad availability and innovative use of a diverse set of computational resources to accelerate fundamental advances in S&E. These investments have spanned discipline-specific instruments and facilities; computational systems and services of varying capabilities and architectures optimized for different applications; virtual organizations for allocating resources and interfacing with users; human expertise in using, developing, deploying and operating resources; and the network backbone that connects and provides access to these resources.

With wide adoption of new modalities of scientific and engineering discovery, the demand for advanced computing capabilities and services has increased significantly over the past two decades. With the slowing of Moore’s Law, advanced computing systems have incorporated more energy-efficient and parallel architectures, along with faster interconnects, and new memory and storage paradigms to enable continued performance growth. This in turn has driven the S&E research community to explore new programming models, algorithms and methods in the pursuit of transformative discoveries across all S&E.

NSF supports several computational resources enabled a broad range of S&E research applications. Many such resources, support for user communities, and integration of novel technologies are coordinated through the NSF-funded “ Advanced Computing Coordination Ecosystem: Services and Support suite of services.

NSF’s ongoing planning for possible future federated cyberinfrastructure resources is guided by long-term visions, such as the National Artificial Intelligence Research Resource (NAIRR) and other elements towards a Future Advanced Computing Ecosystem . NSF’s planning is also driven by advanced computing system usage and performance data from ACCESS monitoring and measurement utilities . Furthermore, NSF recognizes the need to support the growing scale and diversity of the S&E community and to democratize access to computational and data resources for all communities, including underrepresented communities, as informed by the recent NSF-funded study " The missing millions: Democratizing computation and data to bridge digital divides and increase access to science for underrepresented communities ", the “ Geography of Innovation ” (as described in the National Science Board’s Vision 2030 report ), and consistent with the mission of NSF’s Established Program to Stimulate Competitive Research (EPSCoR) in targeted jurisdictions (state, territory or commonwealth).

In the following sections, the term “resource” is used broadly to include systems and/or services.

II. Program Description

The intent of this solicitation is to request proposals from organizations willing to serve as Resource Providers (RPs) within the NSF ACSS program to provide advanced CI capabilities and/or services in production operation to support the full range of computational- and data-intensive research across all of S&E.

To increase the Nation's capacity for transformative S&E discoveries, NSF is interested in continuing to diversify and evolve its portfolio to take advantage of new technologies and services that include capabilities addressing emerging computational- and data-intensive S&E research topics, workflows, and communities, while expanding opportunities for participation by a broader range of potential RPs.

This competition emphasizes the provisioning of an ecosystem of advanced computational resources and services that is responsive to the dramatic increase in the number and nature of applications using NSF-funded resources. Proposals are requested for advanced computing systems and services that will acquire and deploy capabilities and services, including composable services, to address the increase in demand for computation, data analytics and AI resources in the S&E research community, as well as explore novel paradigms for enabling transformative S&E discoveries.

An important aspect of the current solicitation is that funded projects must provide CI capabilities and/or services that demonstrate high degrees of stability and usability during the period of production operations available to the broad S&E community. NSF strongly urges the community to consider expanding the range of possibilities in enabling S&E communities to leverage the power of computation for transformative research, and to think broadly about the nature and composition of the CI ecosystem including next-generation energy use and operational practices for reducing carbon output of research computing. Other considerations may include, but are not limited to, ease of access to proposed resources by new S&E communities; new capabilities that will open up new methods and paradigms for S&E discoveries; federated approaches with opportunities for leveraging the increasing availability and capabilities at the network edge; and composable services provisioning virtualized computing infrastructure and commercial cloud services.

The current solicitation is intended to complement previous NSF investments in advanced computational infrastructure through provisioning resources in two categories as described below.

Category I – Capacity Resources

Resources proposed in this category are intended to be operational deployments of production computational resources that will provide maximum capacity and throughput to support the broad range of computation, data analytics and AI needs in S&E research. The deployments are expected to adhere to a vision of an advanced computing ecosystem as a federated set of resources and services that are heterogeneous in architecture, resource type, and usage mode to collectively meet the Nation’s foundational needs for world-leading computing capabilities.

The proposed resource must be clearly motivated by the current and future demand for simulation, data analysis, and AI use cases for the broad disciplinary and geographically diverse S&E research communities.

Proposers are encouraged to explore novel models for future dynamic national cyberinfrastructure federation including in compute resources, software, data, technical expertise, stakeholders, on-demand allocations, and resource provisioning mechanisms. The latter mechanisms can govern regional and/or campus supported resources, and/or commercial cloud services, enabling comprehensive and effective science-based response to a potential future national and/or international urgent need; or be available to fuel AI research and developments enabling advances towards safe, secure, and trustworthy development and use of artificial intelligence, as elements opening opportunities for the next breakthroughs in science, engineering, and technology. Competitive proposals in Category I must address the following themes in the Project Description (to be discussed in a specific subsection as described in Section V.A. Proposal Preparation Instructions, if noted):

• A clear plan for provisioning a resource that addresses the current and future demand for computational, data analytics, and AI use cases in the broad S&E computational research community;
• A description of how the resource will support S&E research communities that require a national-scale, on-demand, compute, data-analytics and AI resource with a flexible and accessible software environment
• A comprehensive set of system-level performance and reliability metrics, including minimization of carbon output and energy usage, that will be used by NSF for acceptance of the resource or service (to be discussed in the S&E Application Performance and Resource Reliability and Usability sections);
• A detailed risk-mitigated deployment plan to ensure that the proposed resource will be in production operations and available for allocation to the open S&E research community no later than 12 months from the time of award (to be discussed in the Project Management and Risk Mitigation section);
• A clear concept of operations for the project duration with a clear set of operational performance monitoring and science impact metrics to ensure the resource will be an asset for the nation’s S&E research community, as informed by the ACCESS Program’s Integration Roadmaps for new resource providers (to be discussed in the Concept of Operations section);
• A persuasive articulation as to how the resource will support less traditional and/or underrepresented computational S&E communities if appropriate and how models of engagement with campus-supported CI will be explored (to be discussed in the Broader Impacts section).

Relevant parameters contributing to the comprehensive technical description of the proposed system may vary with the nature of the resource. However, organization of the proposal must closely adhere to the guidelines provided in section V.A. Proposal Preparation Instructions.

Category II – Innovative Prototypes/Testbeds

Resources proposed in this category will be initially deployed as a prototype/testbed supporting S&E research through delivery of novel forward-looking capabilities and services. Resources proposed in this category can represent the deployment of new technologies, system architectures, or usage modalities at scale, with plans for developing a national S&E user community that will benefit from the proposed capabilities. Proposed resources could encompass a broad range from enabling of advancements in traditional computing architectures to novel computing paradigms. The former could include novel processor architectures supporting artificial intelligence applications or integration of distributed systems leveraging edge devices; domain-specific architectures; reconfigurable and/or software defined systems; systems designed for streaming data and/or real-time processing, etc. The latter could apply aspects of neural and broader levels of non-neural biological organization architectures or implement collective properties of quantum states. Proposers are further encouraged to potentially explore novel facility scale electric power infrastructure, including models, leading to significant efficiencies in compute center and edge scale power utilization. Additionally, the solicitation incents efforts to explore and assess comprehensive and effective future options for science-based responses to a potential future national and/or international urgent need, as well as towards opportunities for future AI-enabled breakthroughs in science, engineering, and technology.

Proposers must clearly define the target classes of S&E applications that will be enabled, as well as a clear plan for ensuring the widespread adoptions by these classes of applications on the proposed capabilities and/or services. While the resources in this category may initially include prototypes/experimental testbeds, proposers are expected to present a clear near-term plan for transitioning to high-availability production services broadly available and allocatable to the S&E community through open peer-reviewed processes during the final 24 months of the project award period. It is also expected that the initially deployed prototype/testbed will include active engagements with S&E researchers, and these engagements will be reviewed by NSF in its evaluation of the system. Clear science impact metrics for measuring the performance of the proposed system are required.

Competitive proposals in Category II must address the following themes in the Project Description (to be discussed in a specific subsection as described in Section V.A. Proposal Preparation Instructions, if noted):

• A clear plan for provisioning innovative computational and data analysis capabilities or services that will enable new methods and paradigms in support of transformational S&E discoveries;
• A compelling description of how the proposed capabilities or services will address future demand for computation and data analytics capabilities in S&E research;
• A persuasive set of S&E use cases, including quantitative analysis through benchmarks, that clearly motivate how the resource will expand the range of S&E applications that can be currently tackled using existing ACSS resources;
• A clearly defined set of target S&E application classes that will be enabled, as well as a clear plan for ensuring the widespread adoption by these classes of applications on the proposed capabilities and/or services;
• A comprehensive set of system-level performance and reliability metrics that will be used by NSF for acceptance of the resource or service (to be discussed in the S&E Application Performance and Resource Reliability and Usability sections);
• A detailed risk-mitigated deployment plan to ensure that the proposed resource will evolve to high-availability production services broadly available for allocation to the open S&E research community in the final 24 months of the award period (to be discussed in the Project Management and Risk Mitigation section);
• A clear concept of operations for the project duration, with a detailed set of engagement activities with the S&E research community, to optimize the use of the resource, facilitate application and user transition during the initially-deployed prototype/testbed system phase, and ensure that the resource evolves to a high-availability production utility for a national community of S&E users (see the ACCESS Program's Integration Roadmaps for new resource providers) (to be discussed in the Concept of Operations section);

For Both Categories

Proposals are encouraged to emphasize broader impacts and broadening participation engagements for a proposed resource and its operation. Such activities may include (but are not limited to):

• providing access to scientific disciplines and communities traditionally underserved by CI resources and services through, for example, science gateways such as those enabled by the “Center of Excellence to Extend Access”, “Expand the Community”, and “Exemplify Good Practices for CI Through Science Gateways” (SGX3), and/or
• taking into consideration the full lifecycle environmental impact of the proposed resource or services in either category, including its acquisition, usage, and eventual disposal. [Note: this solicitation is not meant to fund core research on sustainability in computing. Proposers interested in proposing such research may refer to NSF 22-060 DCL: “Design for Sustainability in Computing and submission to Computer and Information Science and Engineering: Core Programs ”]

III. Award Information

Category I awards shall be between $10,000,000 and $20,00,000 for up to 5 years of duration.

IV. Eligibility Information

V. proposal preparation and submission instructions.

Full Proposal Preparation Instructions : Proposers may opt to submit proposals in response to this Program Solicitation via Research.gov or Grants.gov.

• Full Proposals submitted via Research.gov: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal and Award Policies and Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg . Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] . The Prepare New Proposal setup will prompt you for the program solicitation number.
• Full proposals submitted via Grants.gov: Proposals submitted in response to this program solicitation via Grants.gov should be prepared and submitted in accordance with the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov . The complete text of the NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: ( https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ). To obtain copies of the Application Guide and Application Forms Package, click on the Apply tab on the Grants.gov site, then click on the Apply Step 1: Download a Grant Application Package and Application Instructions link and enter the funding opportunity number, (the program solicitation number without the NSF prefix) and press the Download Package button. Paper copies of the Grants.gov Application Guide also may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

See PAPPG Chapter II.D.2 for guidance on the required sections of a full research proposal submitted to NSF. Please note that the proposal preparation instructions provided in this program solicitation may deviate from the PAPPG instructions.

The following provides additional guidance beyond that contained in the PAPPG or NSF Grants.gov Application Guide.

1. Cover Sheet:

Proposal titles must begin with “Category I: Title” or “Category II: Title” depending on the type of resource/services proposed.

Only personnel directly connected to the project should be listed as collaborators.

Collaborative efforts may only be submitted as a single proposal (See PAPPG Chapter II.E.3.a), in which a single award is being requested. The involvement of partner organizations should be supported through subawards administered by the proposing Resource Provider organization.

2. Project Description (30-page limit)

The page limit for the Project Description section of the proposal is 30 pages. In addition to the instructions described in the PAPPG or NSF Grants.gov Application Guide, the Project Description must include the following sections:

• Resource Specification;
• S&E Application Performance;
• Resource Reliability and Usability;
• Project Management and Risk Mitigation;
• Concept of Operations; and
• Broader Impacts

Proposals submitted in response to Category I must address the themes described in Section II Project Description Category I – Capacity Resources.

Proposals submitted in response to Category II must address the themes described in Section II Project Description Category II – Innovative Prototypes/Testbeds.

Proposals submitted in response to both categories are encouraged to emphasize broader impacts and broadening participation engagements for a proposed resource and its operation as described in Section II Project Description.

a. Resource Specification

Proposals must describe the resource to be provisioned in this section. The description of the resource architecture should be commensurate with the type of resource being acquired (i.e. hardware and/or software and services) and highlighting any unique components. Aspects of the resource that are likely to influence the performance of S&E applications and workflows should also be specified. This section should also include a description of how the resource will complement and extend the current NSF-funded ACSS ecosystem; how new user communities will be attracted; what new computing methods/paradigms will be possible; and what scientific opportunities will be catalyzed.

Proposals including hardware resources should elaborate on the details of the hardware to be acquired. While relevant specifications will vary depending on the nature of the hardware resource, such details could include the processor and node type, memory hierarchy and bandwidth, inter-connect and I/O subsystem. The description should also include details on the software stack (vendor-supplied or open), container technology, tools, applications as well as system administration monitoring and management capabilities.

Proposals including service resources should elaborate on the details of the services to be acquired, with the understanding that they may differ substantively from the details of a hardware resource acquisition. Such details could include (but are not limited to): software environment, type of services being acquired (e.g., co-location, cloud computing, etc.); method of user access to the proposed services (e.g., interactive, job submission, or other mechanism); hardware specifications of initial computing ‘instances’ provided to users, if applicable; orchestration mechanisms; abstraction layer(s) present between computing capabilities and the user; initial applications that would be hosted by the service; and system administration, monitoring, and management capabilities.

Irrespective of a hardware or service focus, this section must describe the external network connectivity between the proposed resource and national networks, including potential integration with federated and/or distributed resources, regional and/or campus-supported resources, and/or commercial cloud services. S&E research applications can produce many terabytes to petabytes of data. Descriptions of how these data will be handled; how data integrity will be maintained; what backup and contingency procedures and schedules will be implemented; how data accessibility will be facilitated; and how archive storage will be provided, should be included as appropriate. 

b. S&E Application Performance

For the purposes of this solicitation, applications are defined as any S&E computing application, suite of applications, that enables the scientific discovery process. This could include, but is not limited to, a single application, an ensemble/high-throughput model, data-analytic pipelines, or other discovery workflows.

This section should describe the types of S&E research challenges and use cases that motivate the detailed resource design. This description must include the expected impact of the resource to S&E research; the S&E research challenges that motivate the selection for the innovative capabilities; and the expected impact of the specific new innovative capabilities of the resource.

Proposals must provide a compelling justification that explicitly addresses the new innovative capabilities' relevance to S&E. The features of applications motivating the design and configuration of the proposed innovative capability should be fully explained with respect to how the innovation expands the reach to new S&E research and research communities or enables applications that are difficult to address with the current NSF-funded ACSS resources.

Proposals must provide a persuasive set of S&E applications and use cases, including quantitative analyses through benchmarks and/or workflow metrics that clearly motivate how the resource will expand the range of S&E applications that can be currently tackled using existing NSF-funded ACSS.

c. Resource Reliability and Usability

Proposals must describe the types of system usage and job performance data that will be accessible to, and transparently visible from, third-party interfaces currently supported through ACCESS or other NSF-approved alternative.

Proposals must include an analysis of the reliability of the proposed production resource with appropriate justifications. Proposals must provide a detailed analysis of resource utilization goals to ensure that the proposed resource is effective and efficiently used as an instrument for the broader S&E research community.

The NSF award instrument will include a performance requirement on the availability of the resource. NSF requires that, when averaged over a month, production resources should be unavailable as a result of scheduled and unscheduled maintenance no more than 5% of the time. Proposals must provide an analysis establishing the basis supporting the expectation that the proposed system will achieve this performance requirement.

This section should also include descriptions of service level agreements (SLAs) in measurable and verifiable terms. These can include, for example:

• reliability, availability, and usability operating targets (with consideration for planned and unplanned services “downtime”);
• compliance with the policies and other requirements, including with respect to maintenance of cybersecurity, privacy, confidentiality, intellectual property, location of data and/or computations, disclosure/access, and/or disposition (including potentially appropriate deletion/retention) of all processed data and/or computations, etc.;

d. Project Management and Risk Mitigation

Proposals must provide a detailed implementation plan and corresponding independently verifiable metrics for developing and/or acquiring and deploying the proposed resource, including any innovative capabilities. A detailed month-by-month schedule must be provided, including an early operations phase period of not less than 30 consecutive days to demonstrate and confirm the innovative capabilities of the proposed resource.

Proposals must provide details on the sub-contract(s) with the relevant vendor(s) that describe the contractual terms of any substantial acquisition of hardware, software, or services.

Proposals must describe the availability of experts to address any system integration problems that arise as the resource is deployed. This expertise may be provided by the proposing Resource Provider and/or by other vendor, academic, or government partners. Proposers should make clear their previous associations, if any, with these partners. The breadth of knowledge, depth of interaction, and technical abilities of partners will be considered in the review process. This knowledge and expertise are particularly important in supporting advanced programming or usage paradigms tools, system components including I/O subsystems, virtualization, and composable services.

Proposals must describe S&E community user access to the resource during the deployment phase and prior to system acceptance, including during testing.

Proposals must describe the experience of the proposing organization in the management of awards of the similar scale to that being presently proposed and the resources that would be available to manage an award. If the proposal involves a substantial acquisition, describe the experience of the proposing organization in the management of large sub-contracts to vendors for the acquisition of HPC systems. Proposals must describe the organizational resources that would be available to manage any such sub-contract issued under an award made because of this solicitation.

Proposals must provide a detailed risk mitigation plan, identifying both technical and management risks as well as strategies to mitigate such risks. The risk management plan must include risks specific to the innovative capability such as S&E community adoption or sustainability.

e. Security

Proposals must describe both physical and operational security plans for the proposed resource. Proposals must describe project roles and responsibilities with respect to cybersecurity for the facility as well as how risk will be assessed; what technical safeguards will be in place; what administrative safeguards will be maintained; what physical safeguards are planned; how policies and procedures for cybersecurity will be established and maintained; what the plans are for awareness and training; and what procedures will be in place for notification to NSF, the user community, other CI communities, and appropriate authorities(e.g., local police, the Federal Bureau of Investigation). Proposers must describe how the effectiveness of the proposed cybersecurity program will be evaluated and assessed, and what approach will be taken to implement the cybersecurity plan. The section should also discuss how the resource fulfills and advances the NSF priorities for research security .

f. Concept of Operations

Proposals must provide a plan for operations, maintenance, and user support that includes a description of the anticipated requirements of the S&E research community; a description of how resources will be allocated; and any other operational details likely to have an impact on user access or usage of the proposed system. The plans should describe the number and anticipated qualifications of the types of personnel that will be involved with the provision of user support as well as user training that will be provided.

Proposals must describe the experience of the proposing organization in operating production systems, including any experience in operating in a physically distributed environment. This section must include a description of whether operational support was provided on a 24/7 basis or was provided on a more limited basis; the number and types of users; the types of computation performed; and the nature of the user support provided.

The section must include planned success metrics and related data reporting associated with the operations phase of the resource. Such metrics should be flexible and allow for future alignment with peer ACSS RPs, as well as responsive to future guidance from the ACCESS program and/or NSF. Such metrics should include, but are not limited to evaluating management performance, reporting usage and job performance data to the utilities supported by the ACCESS program or its successor, determining user needs, and evaluating user satisfaction.

Proposals must describe the qualifications of the Principal Investigator and co-Principal Investigators regarding her or his ability to manage a project of this size and complexity, as well as manage a resource with a potentially large number of external users.

Proposals must provide an analysis of the annual operating costs of the resource for the duration of the award, including the cost of providing user support. Detailed operating cost estimates should include any necessary maintenance contracts. Operating cost estimates should also include (if applicable) the cost of power and physical security, the cost of external network connectivity from the location(s) of the system to other CI projects, national networks, including to potentially integrate effectively with federated and/or distributed resources, regional and/or campus-supported resources, and/or commercial cloud services, and costs associated with leasing machine room space, if necessary. An estimate of the costs associated with the number of full-time equivalents (FTEs) necessary to maintain 24/7 operations of the proposed system should be provided as well as an estimate of the costs associated with the number of FTEs necessary to provide effective user support. Services leveraged from other CI projects and/or commercial cloud services must also be described.

A more detailed explanation of the budget for user support and operating costs should be provided in the Supplementary Documents section of the proposal (this should not exceed 5 pages). Information provided will be used to help NSF assess the operating cost-performance attributes of the proposed system.

Any other factors that are anticipated to have an impact on the Total Cost of Ownership of the proposed resource must also be provided.

h. Broader Impacts

In addition to the instructions provided in the PAPPG, proposals must describe any complementary and leveraged aspects within the CI ecosystem, with emphasis on other NSF-funded CI projects and priorities described in the Program Description.

3. Budget and Budget Justification

Proposals must include standard yearly and cumulative budget pages as described in the PAPPG. Note that any projected operating costs must NOT be included in this section and should be detailed in the supplementary documents as described below.

4. Supplementary Documents

In addition to the required documents specified in the PAPPG, proposals should include the following as Other Supplementary Documents:

• A list of all institutions and companies involved in the project, together with their roles within the project and the levels of funding.
• Actual or estimated performance benchmark results as described in Section V.A. Proposal Preparation Instructions, S&E Application Performance. This section should not be used to continue discussion or analysis of the merits of the Service Provider, vendor or vendors, or system.
• Detailed Projected Operating Costs as described in Section V.A. Proposal Preparation Instructions, Concept of Operations. This should not exceed 5 pages.
• (Optional, but encouraged) Representative vendor quotes for the proposed resource capability.
• Letters of collaboration from individuals who are described in the Project Description as involved in the project in a senior capacity but who are not members of the lead proposing organization, or from representatives of institutions or organizations collaborating with the lead institution, are allowable, as described in the PAPPG. Note that letters of endorsement should not be included in proposals.
• Any substantial collaboration with individuals not included in the budget should be described in the Facilities, Equipment and Other Resources section of the proposal and documented in a letter of collaboration from each collaborator.

Proprietary information

Proposals containing patentable ideas, trade secrets, and/or privileged or confidential commercial or financial information, disclosure of which may harm the proposer, should be clearly marked where appropriate in the proposal and labeled with the following legend:

• “The following is (proprietary or confidential) information that (name of proposing organization) requests not be released to persons outside the U.S. Federal Government, except for purposes of review and evaluation.”

Note that proposals submitted to this solicitation will be reviewed by a group of experts that include people who are not U.S. Federal Government personnel.

Cost Sharing:

Budget Preparation Instructions:

Each award will support the acquisition and deployment of hardware, software, and associated personnel costs, including acceptance testing. Detailed budgetary information should be provided in the Budget Justification section of the proposal. Operating costs (as described in the required supplementary document) are NOT to be included in the Budget or Budget Justification sections of the proposal.

The proposal amount cannot exceed $20,000,000 for a single Category I award and $5,000,000 for a single Category II award. Acquisition and deployment of the full system should be completed within 12 months of the award start date. The number of years that the proposed system will be deployed can vary with the nature of the system. In most cases, it is anticipated to be part of the NSF-funded ACSS program for up to five years.

Each proposal may be for an acquisition that occurs in one step near the beginning of the award period or for an acquisition that is deployed in phases during the award period.

User support and operating costs of up to 15% of the initial acquisition costs per year, after acceptance of the proposed system, will be funded via consideration of a supplement to the awarded cooperative agreement or other proposal mechanism. Detailed budgetary information should be provided in the Budget Justification section of the proposal.

Prospective PIs are reminded that proposals with budgets exceeding the maximum total will be returned without review. For this purpose, a multi-organization collaborative project is treated as one proposal for which the above limits apply.

D. Research.gov/Grants.gov Requirements

For Proposals Submitted Via Research.gov:

To prepare and submit a proposal via Research.gov, see detailed technical instructions available at: https://www.research.gov/research-portal/appmanager/base/desktop?_nfpb=true&_pageLabel=research_node_display&_nodePath=/researchGov/Service/Desktop/ProposalPreparationandSubmission.html . For Research.gov user support, call the Research.gov Help Desk at 1-800-381-1532 or e-mail [email protected] . The Research.gov Help Desk answers general technical questions related to the use of the Research.gov system. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

For Proposals Submitted Via Grants.gov:

Before using Grants.gov for the first time, each organization must register to create an institutional profile. Once registered, the applicant's organization can then apply for any federal grant on the Grants.gov website. Comprehensive information about using Grants.gov is available on the Grants.gov Applicant Resources webpage: https://www.grants.gov/applicants . In addition, the NSF Grants.gov Application Guide (see link in Section V.A) provides instructions regarding the technical preparation of proposals via Grants.gov. For Grants.gov user support, contact the Grants.gov Contact Center at 1-800-518-4726 or by email: [email protected] . The Grants.gov Contact Center answers general technical questions related to the use of Grants.gov. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this solicitation.

Submitting the Proposal: Once all documents have been completed, the Authorized Organizational Representative (AOR) must submit the application to Grants.gov and verify the desired funding opportunity and agency to which the application is submitted. The AOR must then sign and submit the application to Grants.gov. The completed application will be transferred to Research.gov for further processing.

The NSF Grants.gov Proposal Processing in Research.gov informational page provides submission guidance to applicants and links to helpful resources including the NSF Grants.gov Application Guide , Grants.gov Proposal Processing in Research.gov how-to guide , and Grants.gov Submitted Proposals Frequently Asked Questions . Grants.gov proposals must pass all NSF pre-check and post-check validations in order to be accepted by Research.gov at NSF.

When submitting via Grants.gov, NSF strongly recommends applicants initiate proposal submission at least five business days in advance of a deadline to allow adequate time to address NSF compliance errors and resubmissions by 5:00 p.m. submitting organization's local time on the deadline. Please note that some errors cannot be corrected in Grants.gov. Once a proposal passes pre-checks but fails any post-check, an applicant can only correct and submit the in-progress proposal in Research.gov.

Proposers that submitted via Research.gov may use Research.gov to verify the status of their submission to NSF. For proposers that submitted via Grants.gov, until an application has been received and validated by NSF, the Authorized Organizational Representative may check the status of an application on Grants.gov. After proposers have received an e-mail notification from NSF, Research.gov should be used to check the status of an application.

VI. NSF Proposal Processing And Review Procedures

Proposals received by NSF are assigned to the appropriate NSF program for acknowledgement and, if they meet NSF requirements, for review. All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal. These reviewers are selected by Program Officers charged with oversight of the review process. Proposers are invited to suggest names of persons they believe are especially well qualified to review the proposal and/or persons they would prefer not review the proposal. These suggestions may serve as one source in the reviewer selection process at the Program Officer's discretion. Submission of such names, however, is optional. Care is taken to ensure that reviewers have no conflicts of interest with the proposal. In addition, Program Officers may obtain comments from site visits before recommending final action on proposals. Senior NSF staff further review recommendations for awards. A flowchart that depicts the entire NSF proposal and award process (and associated timeline) is included in PAPPG Exhibit III-1.

A comprehensive description of the Foundation's merit review process is available on the NSF website at: https://www.nsf.gov/bfa/dias/policy/merit_review/ .

Proposers should also be aware of core strategies that are essential to the fulfillment of NSF's mission, as articulated in Leading the World in Discovery and Innovation, STEM Talent Development and the Delivery of Benefits from Research - NSF Strategic Plan for Fiscal Years (FY) 2022 - 2026 . These strategies are integrated in the program planning and implementation process, of which proposal review is one part. NSF's mission is particularly well-implemented through the integration of research and education and broadening participation in NSF programs, projects, and activities.

One of the strategic objectives in support of NSF's mission is to foster integration of research and education through the programs, projects, and activities it supports at academic and research institutions. These institutions must recruit, train, and prepare a diverse STEM workforce to advance the frontiers of science and participate in the U.S. technology-based economy. NSF's contribution to the national innovation ecosystem is to provide cutting-edge research under the guidance of the Nation's most creative scientists and engineers. NSF also supports development of a strong science, technology, engineering, and mathematics (STEM) workforce by investing in building the knowledge that informs improvements in STEM teaching and learning.

NSF's mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering. NSF is committed to this principle of diversity and deems it central to the programs, projects, and activities it considers and supports.

A. Merit Review Principles and Criteria

The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education. To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes." NSF makes every effort to conduct a fair, competitive, transparent merit review process for the selection of projects.

1. Merit Review Principles

These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards. Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply:

• All NSF projects should be of the highest quality and have the potential to advance, if not transform, the frontiers of knowledge.
• NSF projects, in the aggregate, should contribute more broadly to achieving societal goals. These "Broader Impacts" may be accomplished through the research itself, through activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. The project activities may be based on previously established and/or innovative methods and approaches, but in either case must be well justified.
• Meaningful assessment and evaluation of NSF funded projects should be based on appropriate metrics, keeping in mind the likely correlation between the effect of broader impacts and the resources provided to implement projects. If the size of the activity is limited, evaluation of that activity in isolation is not likely to be meaningful. Thus, assessing the effectiveness of these activities may best be done at a higher, more aggregated, level than the individual project.

With respect to the third principle, even if assessment of Broader Impacts outcomes for particular projects is done at an aggregated level, PIs are expected to be accountable for carrying out the activities described in the funded project. Thus, individual projects should include clearly stated goals, specific descriptions of the activities that the PI intends to do, and a plan in place to document the outputs of those activities.

These three merit review principles provide the basis for the merit review criteria, as well as a context within which the users of the criteria can better understand their intent.

2. Merit Review Criteria

All NSF proposals are evaluated through use of the two National Science Board approved merit review criteria. In some instances, however, NSF will employ additional criteria as required to highlight the specific objectives of certain programs and activities.

The two merit review criteria are listed below. Both criteria are to be given full consideration during the review and decision-making processes; each criterion is necessary but neither, by itself, is sufficient. Therefore, proposers must fully address both criteria. (PAPPG Chapter II.D.2.d(i). contains additional information for use by proposers in development of the Project Description section of the proposal). Reviewers are strongly encouraged to review the criteria, including PAPPG Chapter II.D.2.d(i), prior to the review of a proposal.

When evaluating NSF proposals, reviewers will be asked to consider what the proposers want to do, why they want to do it, how they plan to do it, how they will know if they succeed, and what benefits could accrue if the project is successful. These issues apply both to the technical aspects of the proposal and the way in which the project may make broader contributions. To that end, reviewers will be asked to evaluate all proposals against two criteria:

• Intellectual Merit: The Intellectual Merit criterion encompasses the potential to advance knowledge; and
• Broader Impacts: The Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.

The following elements should be considered in the review for both criteria:

• Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and
• Benefit society or advance desired societal outcomes (Broader Impacts)?
• To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?
• Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?
• How well qualified is the individual, team, or organization to conduct the proposed activities?
• Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. NSF values the advancement of scientific knowledge and activities that contribute to achievement of societally relevant outcomes. Such outcomes include, but are not limited to: full participation of women, persons with disabilities, and other underrepresented groups in science, technology, engineering, and mathematics (STEM); improved STEM education and educator development at any level; increased public scientific literacy and public engagement with science and technology; improved well-being of individuals in society; development of a diverse, globally competitive STEM workforce; increased partnerships between academia, industry, and others; improved national security; increased economic competitiveness of the United States; and enhanced infrastructure for research and education.

Proposers are reminded that reviewers will also be asked to review the Data Management and Sharing Plan and the Mentoring Plan, as appropriate.

Additional Solicitation Specific Review Criteria

Reviewers will be asked to assess the adequacy of the descriptions provided in the required sections of the Project Description (these are described in Section V.A. Proposal Preparation Instructions above):

• Broader Impacts.

B. Review and Selection Process

Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review.

Reviewers will be asked to evaluate proposals using two National Science Board approved merit review criteria and, if applicable, additional program specific criteria. A summary rating and accompanying narrative will generally be completed and submitted by each reviewer and/or panel. The Program Officer assigned to manage the proposal's review will consider the advice of reviewers and will formulate a recommendation.

After scientific, technical and programmatic review and consideration of appropriate factors, the NSF Program Officer recommends to the cognizant Division Director whether the proposal should be declined or recommended for award. NSF strives to be able to tell proposers whether their proposals have been declined or recommended for funding within six months. Large or particularly complex proposals or proposals from new recipients may require additional review and processing time. The time interval begins on the deadline or target date, or receipt date, whichever is later. The interval ends when the Division Director acts upon the Program Officer's recommendation.

After programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements or the Division of Acquisition and Cooperative Support for review of business, financial, and policy implications. After an administrative review has occurred, Grants and Agreements Officers perform the processing and issuance of a grant or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with a NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

Once an award or declination decision has been made, Principal Investigators are provided feedback about their proposals. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers or any reviewer-identifying information, are sent to the Principal Investigator/Project Director by the Program Officer. In addition, the proposer will receive an explanation of the decision to award or decline funding.

VII. Award Administration Information

A. notification of the award.

Notification of the award is made to the submitting organization by an NSF Grants and Agreements Officer. Organizations whose proposals are declined will be advised as promptly as possible by the cognizant NSF Program administering the program. Verbatim copies of reviews, not including the identity of the reviewer, will be provided automatically to the Principal Investigator. (See Section VI.B. for additional information on the review process.)

B. Award Conditions

An NSF award consists of: (1) the award notice, which includes any special provisions applicable to the award and any numbered amendments thereto; (2) the budget, which indicates the amounts, by categories of expense, on which NSF has based its support (or otherwise communicates any specific approvals or disapprovals of proposed expenditures); (3) the proposal referenced in the award notice; (4) the applicable award conditions, such as Grant General Conditions (GC-1)*; or Research Terms and Conditions* and (5) any announcement or other NSF issuance that may be incorporated by reference in the award notice. Cooperative agreements also are administered in accordance with NSF Cooperative Agreement Financial and Administrative Terms and Conditions (CA-FATC) and the applicable Programmatic Terms and Conditions. NSF awards are electronically signed by an NSF Grants and Agreements Officer and transmitted electronically to the organization via e-mail.

*These documents may be accessed electronically on NSF's Website at https://www.nsf.gov/awards/managing/award_conditions.jsp?org=NSF . Paper copies may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

More comprehensive information on NSF Award Conditions and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

Administrative and National Policy Requirements

Build America, Buy America

As expressed in Executive Order 14005, Ensuring the Future is Made in All of America by All of America’s Workers (86 FR 7475), it is the policy of the executive branch to use terms and conditions of Federal financial assistance awards to maximize, consistent with law, the use of goods, products, and materials produced in, and services offered in, the United States.

Consistent with the requirements of the Build America, Buy America Act (Pub. L. 117-58, Division G, Title IX, Subtitle A, November 15, 2021), no funding made available through this funding opportunity may be obligated for infrastructure projects under an award unless all iron, steel, manufactured products, and construction materials used in the project are produced in the United States. For additional information, visit NSF’s Build America, Buy America webpage.

The cooperative agreements will have an extensive section of Special Conditions relating to the period of performance, Project Execution Plan including plan to develop performance metrics, recipient responsibilities, NSF responsibilities, joint NSF- recipient responsibilities, funding and funding schedule, reporting requirements, key personnel, and other conditions. NSF has responsibility for providing general oversight and monitoring of the project(s) to help assure effective performance and administration, as well as facilitating any coordination among the recipient as necessary to further the objectives of the program. Within the first 90 days of the Award, a revised project execution plan will be submitted to NSF for concurrence.

Awards made as a result of this competition will include performance requirements and metrics for the proposed systems. If appropriate, a recipient will include terms and conditions in any subaward agreement to address schedule and performance expectations and the impact of delays in delivery.

C. Reporting Requirements

For all multi-year grants (including both standard and continuing grants), the Principal Investigator must submit an annual project report to the cognizant Program Officer no later than 90 days prior to the end of the current budget period. (Some programs or awards require submission of more frequent project reports). No later than 120 days following expiration of a grant, the PI also is required to submit a final annual project report, and a project outcomes report for the general public.

Failure to provide the required annual or final annual project reports, or the project outcomes report, will delay NSF review and processing of any future funding increments as well as any pending proposals for all identified PIs and co-PIs on a given award. PIs should examine the formats of the required reports in advance to assure availability of required data.

PIs are required to use NSF's electronic project-reporting system, available through Research.gov, for preparation and submission of annual and final annual project reports. Such reports provide information on accomplishments, project participants (individual and organizational), publications, and other specific products and impacts of the project. Submission of the report via Research.gov constitutes certification by the PI that the contents of the report are accurate and complete. The project outcomes report also must be prepared and submitted using Research.gov. This report serves as a brief summary, prepared specifically for the public, of the nature and outcomes of the project. This report will be posted on the NSF website exactly as it is submitted by the PI.

More comprehensive information on NSF Reporting Requirements and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

Additional reporting requirements will be negotiated with the Resource Provider prior to award and will be incorporated into the special terms and conditions of the award. Such requirements may include, for example, monthly and quarterly reports reverse-/site visits, and other requirements to enable NSF oversight of the award. The level of oversight will be appropriate to the complexity of the award.

VIII. Agency Contacts

Please note that the program contact information is current at the time of publishing. See program website for any updates to the points of contact.

General inquiries regarding this program should be made to:

For questions related to the use of NSF systems contact:

• NSF Help Desk: 1-800-381-1532
• Research.gov Help Desk e-mail: [email protected]

For questions relating to Grants.gov contact:

Grants.gov Contact Center: If the Authorized Organizational Representatives (AOR) has not received a confirmation message from Grants.gov within 48 hours of submission of application, please contact via telephone: 1-800-518-4726; e-mail: [email protected] .

IX. Other Information

The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities. Use of this website by potential proposers is strongly encouraged. In addition, "NSF Update" is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences . Subscribers are informed through e-mail or the user's Web browser each time new publications are issued that match their identified interests. "NSF Update" also is available on NSF's website .

Grants.gov provides an additional electronic capability to search for Federal government-wide grant opportunities. NSF funding opportunities may be accessed via this mechanism. Further information on Grants.gov may be obtained at https://www.grants.gov .

About The National Science Foundation

The National Science Foundation (NSF) is an independent Federal agency created by the National Science Foundation Act of 1950, as amended (42 USC 1861-75). The Act states the purpose of the NSF is "to promote the progress of science; [and] to advance the national health, prosperity, and welfare by supporting research and education in all fields of science and engineering."

NSF funds research and education in most fields of science and engineering. It does this through grants and cooperative agreements to more than 2,000 colleges, universities, K-12 school systems, businesses, informal science organizations and other research organizations throughout the US. The Foundation accounts for about one-fourth of Federal support to academic institutions for basic research.

NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for graduate and postdoctoral fellowships. The agency operates no laboratories itself but does support National Research Centers, user facilities, certain oceanographic vessels and Arctic and Antarctic research stations. The Foundation also supports cooperative research between universities and industry, US participation in international scientific and engineering efforts, and educational activities at every academic level.

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects. See the NSF Proposal & Award Policies & Procedures Guide Chapter II.F.7 for instructions regarding preparation of these types of proposals.

The National Science Foundation has Telephonic Device for the Deaf (TDD) and Federal Information Relay Service (FIRS) capabilities that enable individuals with hearing impairments to communicate with the Foundation about NSF programs, employment or general information. TDD may be accessed at (703) 292-5090 and (800) 281-8749, FIRS at (800) 877-8339.

The National Science Foundation Information Center may be reached at (703) 292-5111.

The National Science Foundation promotes and advances scientific progress in the United States by competitively awarding grants and cooperative agreements for research and education in the sciences, mathematics, and engineering. To get the latest information about program deadlines, to download copies of NSF publications, and to access abstracts of awards, visit the NSF Website at 2415 Eisenhower Avenue, Alexandria, VA 22314 (NSF Information Center) (703) 292-5111 (703) 292-5090 Send an e-mail to: or telephone: (703) 292-8134 (703) 292-5111 Privacy Act And Public Burden Statements The information requested on proposal forms and project reports is solicited under the authority of the National Science Foundation Act of 1950, as amended. The information on proposal forms will be used in connection with the selection of qualified proposals; and project reports submitted by proposers will be used for program evaluation and reporting within the Executive Branch and to Congress. The information requested may be disclosed to qualified reviewers and staff assistants as part of the proposal review process; to proposer institutions/grantees to provide or obtain data regarding the proposal review process, award decisions, or the administration of awards; to government contractors, experts, volunteers and researchers and educators as necessary to complete assigned work; to other government agencies or other entities needing information regarding proposers or nominees as part of a joint application review process, or in order to coordinate programs or policy; and to another Federal agency, court, or party in a court or Federal administrative proceeding if the government is a party. Information about Principal Investigators may be added to the Reviewer file and used to select potential candidates to serve as peer reviewers or advisory committee members. See System of Record Notices , NSF-50 , "Principal Investigator/Proposal File and Associated Records," and NSF-51 , "Reviewer/Proposal File and Associated Records.” Submission of the information is voluntary. Failure to provide full and complete information, however, may reduce the possibility of receiving an award. An agency may not conduct or sponsor, and a person is not required to respond to, an information collection unless it displays a valid Office of Management and Budget (OMB) control number. The OMB control number for this collection is 3145-0058. Public reporting burden for this collection of information is estimated to average 120 hours per response, including the time for reviewing instructions. Send comments regarding the burden estimate and any other aspect of this collection of information, including suggestions for reducing this burden, to: Suzanne H. 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Uncovering Learners' Experiences to New Normal Education: Implications The new normal education policy in response to the pandemic crisis pushed institutions to shift from traditional face-to-face to asynchronous instruction that posed challenges particularly to science courses in higher education. ... The general recommendations of this study were improving asynchronous instruction delivery through teachers ... PDF The New Normal in Education A Publication of the Institute of Industry and Academic Research Incorporated www.iiari.org 1 INTERNATIONAL JOURNAL OF EDUCATIONAL MANAGEMENT AND DEVELOPMENT STUDIES Volume 1, Issue 1 · September 2020 · ISSN 2719-0633 (PRINT) 2719-0641 (ONLINE) THE NEW NORMAL IN EDUCATION: A CHALLENGE TO THE PRIVATE BASIC EDUCATION INSTITUTIONS IN THE Teaching and Learning in the New Normal: Responding to ... McGaughey, F., et al.: This can't be the new norm': academics' perspectives on the COVID-19 crisis for the Australian university sector. 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(PDF) The COVID-19 Pandemic through the Lens of Education in the One of the most affected is the educational sectors. The COVID- 19. pandemic is still existent today, and there are no specific vaccines or. medicines to eradicate this disease. We need to live to ... COVID-19 and the Educational Response: New Educational and ... This research topic inquires into multiple and diverse impacts of the Covid-19 pandemic on education within various international contexts as billions navigate new educational and social realities. This crisis has led educators at all levels of PreK-20 and their stakeholders to question basic premises about the educational system. Indeed, taken-for-granted educational experiences have been ... Advanced Technological Education (ATE) NSF's mission is to advance the progress of science, a mission accomplished by funding proposals for research and education made by scientists, engineers, and educators from across the country. EVENT: Live from the Arctic Each year, NSF receives more than 40,000 competitive proposals and makes about 11,000 new awards. Those awards include support for cooperative research with industry, Arctic and Antarctic research and operations, and U.S. participation in international scientific efforts. Get News Updates by Email . Connect with us online NSF website: nsf.gov PDF The "new normal" in education The "new normal" in education is the technological order—a passive technologization—and its expansion continues uncontested and even accelerated by the pandemic. Two Greek concepts, kronos and kairos, allow a discussion of contrasts between the quantitative and the qualitative in education. US Department of Education proposals: preparing for the "new normal" Since spring 2014, U.S. Department of Education educational programming grant solicitations have included many new patterns. To understand the changes taking place and to identify common themes ... Educating and Funding Digestive Diseases Research Through the Pilot and New investigators (as defined by NIH guidelines) without independent extramural grant support (including federal, R01, R00, U01, P01, DoD, VA merit or equivalent, and excluding career-development awards, such as K, AGA, AASLD, ALF, and Crohn's and Colitis Foundation of America) who seek to establish independence in the field of gastrointestinal (GI), liver and pancreatic disease research. Program Solicitation NSF 24-583 The Prepare New Proposal setup will prompt you for the program solicitation number. Full proposals submitted via Grants.gov: ... NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for ... 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