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26 papers with code • 1 benchmarks • 1 datasets

Essay scoring: Automated Essay Scoring is the task of assigning a score to an essay, usually in the context of assessing the language ability of a language learner. The quality of an essay is affected by the following four primary dimensions: topic relevance, organization and coherence, word usage and sentence complexity, and grammar and mechanics.

Source: A Joint Model for Multimodal Document Quality Assessment

Benchmarks Add a Result

Most implemented papers, automated essay scoring based on two-stage learning.

Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e. g. the essays composed of permuted sentences and the prompt-irrelevant essays.

A Neural Approach to Automated Essay Scoring

nusnlp/nea • EMNLP 2016

SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring

Our new method proposes a new \textsc{SkipFlow} mechanism that models relationships between snapshots of the hidden representations of a long short-term memory (LSTM) network as it reads.

Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

Youmna-H/Coherence_AES • NAACL 2018

We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences.

Co-Attention Based Neural Network for Source-Dependent Essay Scoring

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring.

Language models and Automated Essay Scoring

In this paper, we present a new comparative study on automatic essay scoring (AES).

Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems

midas-research/calling-out-bluff • 14 Jul 2020

This number is increasing further due to COVID-19 and the associated automation of education and testing.

Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring

Cross-prompt automated essay scoring (AES) requires the system to use non target-prompt essays to award scores to a target-prompt essay.

Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays

To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits.

EXPATS: A Toolkit for Explainable Automated Text Scoring

octanove/expats • 7 Apr 2021

Automated text scoring (ATS) tasks, such as automated essay scoring and readability assessment, are important educational applications of natural language processing.

e-rater ®  Scoring Engine

Evaluates students’ writing proficiency with automatic scoring and feedback

Selection an option below to learn more.

About the e-rater Scoring Engine

The e-rater automated scoring engine uses AI technology and Natural Language Processing (NLP) to evaluate the writing proficiency of student essays by providing automatic scoring and feedback. The engine provides descriptive feedback on the writer’s grammar, mechanics, word use and complexity, style, organization and more.

Who uses the e-rater engine and why?

Companies and institutions use this patented technology to power their custom applications.

The e-rater engine is used within the  Criterion ®  Online Writing Evaluation Service . Students use the e-rater engine's feedback to evaluate their essay-writing skills and to identify areas that need improvement. Teachers use the Criterion service to help their students develop their writing skills independently and receive automated, constructive feedback. The e-rater engine is also used in other low-stakes practice tests include TOEFL ®  Practice Online and GRE ®  ScoreItNow!™.

In high-stakes settings, the engine is used in conjunction with human ratings for both the Issue and Argument prompts of the GRE test's Analytical Writing section and the TOEFL iBT ®  test's Independent and Integrated Writing prompts. ETS research has shown that combining automated and human essay scoring demonstrates assessment score reliability and measurement benefits.

For more information about the use of the e-rater engine, read  E-rater as a Quality Control on Human Scores (PDF) .

How does the e-rater engine grade essays?

The e-rater engine provides a holistic score for an essay that has been entered into the computer electronically. It also provides real-time diagnostic feedback about grammar, usage, mechanics, style and organization, and development. This feedback is based on NLP research specifically tailored to the analysis of student responses and is detailed in  ETS's research publications (PDF) .

How does the e-rater engine compare to human raters?

The e-rater engine uses NLP to identify features relevant to writing proficiency in training essays and their relationship with human scores. The resulting scoring model, which assigns weights to each observed feature, is stored offline in a database that can then be used to score new essays according to the same formula.

The e-rater engine doesn’t have the ability to read so it can’t evaluate essays the same way that human raters do. However, the features used in e-rater scoring have been developed to be as substantively meaningful as they can be, given the state of the art in NLP. They also have been developed to demonstrate strong reliability — often greater reliability than human raters themselves.

Learn more about  how it works .

About Natural Language Processing

The e-rater engine is an artificial intelligence engine that uses Natural Language Processing (NLP), a field of computer science and linguistics that uses computational methods to analyze characteristics of a text. NLP methods support such burgeoning application areas as machine translation, speech recognition and information retrieval.

Ready to begin? Contact us to learn how the e-rater service can enhance your existing program.

Ahead of the Curve: How PEG™ Has Led Automated Scoring for Years

What is peg™.

PEG, or Project Essay Grade, is the automated scoring system at the core of ERB Writing Practice.  It was invented in the 1960s by Ellis Batten Page, a former high school English teacher, who spent “many long weekends sifting through stacks of papers wishing for some help.” His guiding principles? 1) the more we write, the better writer we become, and 2) computers can grade as reliably as their human counterparts (Page, 2003).  The state of computers at the time of Page’s invention did not leave much room for automation, so PEG lay dormant until the mid-1980s.  Given that Page’s two principles are still as relevant today as they were then, PEG was given new life in the 1990s scoring essays for NAEP, Praxis, and GRE testing programs when computerization became feasible.  PEG was eventually acquired by ERB’s longtime partner, Measurement Inc., and continues to evolve and find new uses today.

The foundational concept of automated scoring is that good writing can be predicted .  PEG and other systems require training essays that have human scores, and these systems use such essays to create scoring (or prediction) models.  The models typically include 30-40 features, or variables, within a set of essays that predict human ratings.  Typical examples of such variables include sentence length, use of higher-level vocabulary, and grammar.  In most instances, the combination of these variables yields correlations with human raters in the mid .80s on a scale of 0-1, which is a high level of prediction accuracy—one that is typically higher than correlations between different human raters and themselves.  Once the model is trained, the automated scoring system “reads” subsequent essays, quantifies values for them on each variable in the model, and uses the prediction model to score the essay. 

Despite the proven accuracy of automated scoring systems, a common criticism is that the scores such systems produce lack an understanding of the meaning of a student-written essay.  Humans can rate the quality of an idea or the strength of an argument in ways that computers cannot, even if such ratings can be idiosyncratic and inconsistent at times.  While that criticism is valid, the 30-40 variables used by PEG represent the traits and skills of good writing, and thus are extremely relevant to budding writers who need feedback to learn how to improve their writing as they practice.  To balance out the automated PEG feedback, ERB Writing Practice also includes options for users to collect feedback from peers and/or teachers.  Teachers can give quick, quantitative ratings on how effectively students used textual evidence as well as how accurate the content of their writing is in relation to a given prompt topic. 

When PEG was first used operationally, its focus was on predicting scores holistically; that is, recovering the overall writing score a human assigned the essay.  Over time, scoring evolved to provide feedback on unique traits of effective writing, and different scoring algorithms were developed for distinct genres.  Today, PEG provides scores on six characteristics of writing and uses separate models for three genres: argumentative, informational/explanatory, and narrative. The six characteristics of effective writing that PEG provides scores on are outlined below (learn more at support.wpponline.com ).

• Development of Ideas — The writer’s presentation of supportive details and information pertinent to support their idea.
• Organization — The writer’s overall plan (coherence) and internal weaving together of ideas (cohesion).
• Style — The use of strong word choices and varied sentence constructions to establish a unique voice that connects with the audience.
• Word choice — The appropriate use of advanced vocabulary, precision, and application of vocabulary to an essay.
• Sentence fluency — The use of complex and varied sentences to skillfully create a smooth flow of ideas.
• Conventions — Conventions include grammar, usage, pronoun reference, consistency in number and person, and mechanics (spelling, capitalization, punctuation, and paragraphing).

The strong reliability of PEG scoring for different genres has also enabled teachers to introduce their prompts for automated essay scoring.  When teachers do so, they can select the PEG model that aligns with the genre of their writing prompt, ensuring more nuanced automated scoring. 

Since the advent of PEG, other automated essay scoring systems have been launched, and research has been conducted.  In a recent study conducted by the National Center of Education Statistics, PEG was shown to be the most accurate among automated scoring alternatives at scoring prompts developed for The Nation’s Report Card (NCES, 2022).  Research has also focused on the efficacy of writing practice with PEG scoring.  An important study found that after “controlling for students’ initial writing quality and the amount they used PEG writing, students who used PEG produced higher quality essays at the end of the intervention … 22% higher than those who didn’t” (Palermo, 2018).

So what does this all mean for ERB members?

Our purpose at ERB is to provide member schools with scientifically developed measures they can use to understand gaps in curriculum and instruction, as well as specific areas where students can improve.  ERB Writing Practice is a new program that provides students and educators with a steady stream of reliable data they can use to target improvements to individual writing.  It addresses the enormous time commitment for teachers of grading papers by hand.  ERB Writing Practice also has the evidence to support its efficacy to improve their writing. These benefits open many opportunities for students to write more, and in doing so, become better writers.  

Page, E. B. (2003).  Project Essay Grade: PEG.  In Automated Scoring: A Cross-Disciplinary Perspective (edited by M. Shermis and J. Burstein). Mahwah, NJ: Erlbaum.

Palermo, C. (2018).  Research student finds using PEG writing helps students write higher quality essays.  Retrieved August 12, 2022 from: https://measurementinc.com/news/research-study-finds-using-peg-writing-helps-students-write-higher-quality-essays .

NCES.  (January 21, 2022).  Four Teams Win Top Prize in Automated Scoring Challenge for The Nation’s Report Card.  Retrieved August 12, 2022 from: https://nces.ed.gov/whatsnew/press_releases/1_21_2022.asp

Contact your Member Services Director or Submit a request form if you have questions about ERB Writing Practice.

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Computer Science > Computation and Language

Title: automated essay scoring using efficient transformer-based language models.

Abstract: Automated Essay Scoring (AES) is a cross-disciplinary effort involving Education, Linguistics, and Natural Language Processing (NLP). The efficacy of an NLP model in AES tests it ability to evaluate long-term dependencies and extrapolate meaning even when text is poorly written. Large pretrained transformer-based language models have dominated the current state-of-the-art in many NLP tasks, however, the computational requirements of these models make them expensive to deploy in practice. The goal of this paper is to challenge the paradigm in NLP that bigger is better when it comes to AES. To do this, we evaluate the performance of several fine-tuned pretrained NLP models with a modest number of parameters on an AES dataset. By ensembling our models, we achieve excellent results with fewer parameters than most pretrained transformer-based models.

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Automated Essay Grading

State-of-the-art machine learning framework for automatically grading student essays.

Project Details

• Product: Machine learning framework for automated essay grading
• Technologies: Python, Javascript, Flask, NLTK, SciPy, Deep Learning, Cloud Infrastructure

In close collaboration with our client, a global education company, Boulder Labs designed and implemented a state of the art machine learning system with cloud infrastructure for automated essay grading.

More About this project

We developed a system to automate and streamline much of the work involved in building models to perform automated essay grading. The system includes an API for data collection and validation, tools to automate the modeling process and facilitate research, an interface for reporting on modeling performance, and support for deploying trained models to a production environment.

The system is useful to both the R&D and engineering teams, and facilitates an easy transfer of technology from research to production.

As a separate project for the same client, Boulder Labs developed a grammar checker specifically tailored for student writing. The new system provides a natural interface for integrating external tools, including this grammar checker, into the automated essays grading system.

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Original research article, explainable automated essay scoring: deep learning really has pedagogical value.

• School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Edmonton, AB, Canada

Automated essay scoring (AES) is a compelling topic in Learning Analytics for the primary reason that recent advances in AI find it as a good testbed to explore artificial supplementation of human creativity. However, a vast swath of research tackles AES only holistically; few have even developed AES models at the rubric level, the very first layer of explanation underlying the prediction of holistic scores. Consequently, the AES black box has remained impenetrable. Although several algorithms from Explainable Artificial Intelligence have recently been published, no research has yet investigated the role that these explanation models can play in: (a) discovering the decision-making process that drives AES, (b) fine-tuning predictive models to improve generalizability and interpretability, and (c) providing personalized, formative, and fine-grained feedback to students during the writing process. Building on previous studies where models were trained to predict both the holistic and rubric scores of essays, using the Automated Student Assessment Prize’s essay datasets, this study focuses on predicting the quality of the writing style of Grade-7 essays and exposes the decision processes that lead to these predictions. In doing so, it evaluates the impact of deep learning (multi-layer perceptron neural networks) on the performance of AES. It has been found that the effect of deep learning can be best viewed when assessing the trustworthiness of explanation models. As more hidden layers were added to the neural network, the descriptive accuracy increased by about 10%. This study shows that faster (up to three orders of magnitude) SHAP implementations are as accurate as the slower model-agnostic one. It leverages the state-of-the-art in natural language processing, applying feature selection on a pool of 1592 linguistic indices that measure aspects of text cohesion, lexical diversity, lexical sophistication, and syntactic sophistication and complexity. In addition to the list of most globally important features, this study reports (a) a list of features that are important for a specific essay (locally), (b) a range of values for each feature that contribute to higher or lower rubric scores, and (c) a model that allows to quantify the impact of the implementation of formative feedback.

Automated essay scoring (AES) is a compelling topic in Learning Analytics (LA) for the primary reason that recent advances in AI find it as a good testbed to explore artificial supplementation of human creativity. However, a vast swath of research tackles AES only holistically; only a few have even developed AES models at the rubric level, the very first layer of explanation underlying the prediction of holistic scores ( Kumar et al., 2017 ; Taghipour, 2017 ; Kumar and Boulanger, 2020 ). None has attempted to explain the whole decision process of AES, from holistic scores to rubric scores and from rubric scores to writing feature modeling. Although several algorithms from XAI (explainable artificial intelligence) ( Adadi and Berrada, 2018 ; Murdoch et al., 2019 ) have recently been published (e.g., LIME, SHAP) ( Ribeiro et al., 2016 ; Lundberg and Lee, 2017 ), no research has yet investigated the role that these explanation models (trained on top of predictive models) can play in: (a) discovering the decision-making process that drives AES, (b) fine-tuning predictive models to improve generalizability and interpretability, and (c) providing teachers and students with personalized, formative, and fine-grained feedback during the writing process.

One of the key anticipated benefits of AES is the elimination of human bias such as rater fatigue, rater’s expertise, severity/leniency, scale shrinkage, stereotyping, Halo effect, rater drift, perception difference, and inconsistency ( Taghipour, 2017 ). At its turn, AES may suffer from its own set of biases (e.g., imperfections in training data, spurious correlations, overrepresented minority groups), which has incited the research community to look for ways to make AES more transparent, accountable, fair, unbiased, and consequently trustworthy while remaining accurate. This required changing the perception that AES is merely a machine learning and feature engineering task ( Madnani et al., 2017 ; Madnani and Cahill, 2018 ). Hence, researchers have advocated that AES should be seen as a shared task requiring several methodological design decisions along the way such as curriculum alignment, construction of training corpora, reliable scoring process, and rater performance evaluation, where the goal is to build and deploy fair and unbiased scoring models to be used in large-scale assessments and classroom settings ( Rupp, 2018 ; West-Smith et al., 2018 ; Rupp et al., 2019 ). Unfortunately, although these measures are intended to design reliable and valid AES systems, they may still fail to build trust among users, keeping the AES black box impenetrable for teachers and students.

It has been previously recognized that divergence of opinion among human and machine graders has been only investigated superficially ( Reinertsen, 2018 ). So far, researchers investigated the characteristics of essays through qualitative analyses which ended up rejected by AES systems (requiring a human to score them) ( Reinertsen, 2018 ). Others strived to justify predicted scores by identifying essay segments that actually caused the predicted scores. In spite of the fact that these justifications hinted at and quantified the importance of these spatial cues, they did not provide any feedback as to how to improve those suboptimal essay segments ( Mizumoto et al., 2019 ).

Related to this study and the work of Kumar and Boulanger (2020) is Revision Assistant, a commercial AES system developed by Turnitin ( Woods et al., 2017 ; West-Smith et al., 2018 ), which in addition to predicting essays’ holistic scores provides formative, rubric-specific, and sentence-level feedback over multiple drafts of a student’s essay. The implementation of Revision Assistant moved away from the traditional approach to AES, which consists in using a limited set of features engineered by human experts representing only high-level characteristics of essays. Like this study, it rather opted for including a large number of low-level writing features, demonstrating that expert-designed features are not required to produce interpretable predictions. Revision Assistant’s performance was reported on two essay datasets, one of which was the Automated Student Assessment Prize (ASAP) 1 dataset. However, performance on the ASAP dataset was reported in terms of quadratic weighted kappa and this for holistic scores only. Models predicting rubric scores were trained only with the other dataset which was hosted on and collected through Revision Assistant itself.

In contrast to feature-based approaches like the one adopted by Revision Assistant, other AES systems are implemented using deep neural networks where features are learned during model training. For example, Taghipour (2017) in his doctoral dissertation leverages a recurrent neural network to improve accuracy in predicting holistic scores, implement rubric scoring (i.e., organization and argument strength), and distinguish between human-written and computer-generated essays. Interestingly, Taghipour compared the performance of his AES system against other AES systems using the ASAP corpora, but he did not use the ASAP corpora when it came to train rubric scoring models although ASAP provides two corpora provisioning rubric scores (#7 and #8). Finally, research was also undertaken to assess the generalizability of rubric-based models by performing experiments across various datasets. It was found that the predictive power of such rubric-based models was related to how much the underlying feature set covered a rubric’s criteria ( Rahimi et al., 2017 ).

Despite their numbers, rubrics (e.g., organization, prompt adherence, argument strength, essay length, conventions, word choices, readability, coherence, sentence fluency, style, audience, ideas) are usually investigated in isolation and not as a whole, with the exception of Revision Assistant which provides feedback at the same time on the following five rubrics: claim, development, audience, cohesion, and conventions. The literature reveals that rubric-specific automated feedback includes numerical rubric scores as well as recommendations on how to improve essay quality and correct errors ( Taghipour, 2017 ). Again, except for Revision Assistant which undertook a holistic approach to AES including holistic and rubric scoring and provision of rubric-specific feedback at the sentence level, AES has generally not been investigated as a whole or as an end-to-end product. Hence, the AES used in this study and developed by Kumar and Boulanger (2020) is unique in that it uses both deep learning (multi-layer perceptron neural network) and a huge pool of linguistic indices (1592), predicts both holistic and rubric scores, explaining holistic scores in terms of rubric scores, and reports which linguistic indices are the most important by rubric. This study, however, goes one step further and showcases how to explain the decision process behind the prediction of a rubric score for a specific essay, one of the main AES limitations identified in the literature ( Taghipour, 2017 ) that this research intends to address, at least partially.

Besides providing explanations of predictions both globally and individually, this study not only goes one step further toward the automated provision of formative feedback but also does so in alignment with the explanation model and the predictive model, allowing to better map feedback to the actual characteristics of an essay. Woods et al. (2017) succeeded in associating sentence-level expert-derived feedback with strong/weak sentences having the greatest influence on a rubric score based on the rubric, essay score, and the sentence characteristics. While Revision Assistant’s feature space consists of counts and binary occurrence indicators of word unigrams, bigrams and trigrams, character four-grams, and part-of-speech bigrams and trigrams, they are mainly textual and locational indices; by nature they are not descriptive or self-explanative. This research fills this gap by proposing feedback based on a set of linguistic indices that can encompass several sentences at a time. However, the proposed approach omits locational hints, leaving the merging of the two approaches as the next step to be addressed by the research community.

Although this paper proposes to extend the automated provision of formative feedback through an interpretable machine learning method, it rather focuses on the feasibility of automating it in the context of AES instead of evaluating the pedagogical quality (such as the informational and communicational value of feedback messages) or impact on students’ writing performance, a topic that will be kept for an upcoming study. Having an AES system that is capable of delivering real-time formative feedback sets the stage to investigate (1) when feedback is effective, (2) the types of feedback that are effective, and (3) whether there exist different kinds of behaviors in terms of seeking and using feedback ( Goldin et al., 2017 ). Finally, this paper omits describing the mapping between the AES model’s linguistic indices and a pedagogical language that is easily understandable by students and teachers, which is beyond its scope.

Methodology

This study showcases the application of the PDR framework ( Murdoch et al., 2019 ), which provides three pillars to describe interpretations in the context of the data science life cycle: P redictive accuracy, D escriptive accuracy, and R elevancy to human audience(s). It is important to note that in a broader sense both terms “explainable artificial intelligence” and “interpretable machine learning” can be used interchangeably with the following meaning ( Murdoch et al., 2019 ): “the use of machine-learning models for the extraction of relevant knowledge about domain relationships contained in data.” Here “predictive accuracy” refers to the measurement of a model’s ability to fit data; “descriptive accuracy” is the degree at which the relationships learned by a machine learning model can be objectively captured; and “relevant knowledge” implies that a particular audience gets insights into a chosen domain problem that guide its communication, actions, and discovery ( Murdoch et al., 2019 ).

In the context of this article, formative feedback that assesses students’ writing skills and prescribes remedial writing strategies is the relevant knowledge sought for, whose effectiveness on students’ writing performance will be validated in an upcoming study. However, the current study puts forward the tools and evaluates the feasibility to offer this real-time formative feedback. It also measures the predictive and descriptive accuracies of AES and explanation models, two key components to generate trustworthy interpretations ( Murdoch et al., 2019 ). Naturally, the provision of formative feedback is dependent on the speed of training and evaluating new explanation models every time a new essay is ingested by the AES system. That is why this paper investigates the potential of various SHAP implementations for speed optimization without compromising the predictive and descriptive accuracies. This article will show how the insights generated by the explanation model can serve to debug the predictive model and contribute to enhance the feature selection and/or engineering process ( Murdoch et al., 2019 ), laying the foundation for the provision of actionable and impactful pieces of knowledge to educational audiences, whose relevancy will be judged by the human stakeholders and estimated by the magnitude of resulting changes.

Figure 1 overviews all the elements and steps encompassed by the AES system in this study. The following subsections will address each facet of the overall methodology, from hyperparameter optimization to relevancy to both students and teachers.

Figure 1. A flow chart exhibiting the sequence of activities to develop an end-to-end AES system and how the various elements work together to produce relevant knowledge to the intended stakeholders.

Automated Essay Scoring System, Dataset, and Feature Selection

As previously mentioned, this paper reuses the AES system developed by Kumar and Boulanger (2020) . The AES models were trained using the ASAP’s seventh essay corpus. These narrative essays were written by Grade-7 students in the setting of state-wide assessments in the United States and had an average length of 171 words. Students were asked to write a story about patience. Kumar and Boulanger’s work consisted in training a predictive model for each of the four rubrics according to which essays were graded: ideas, organization, style, and conventions. Each essay was scored by two human raters on a 0−3 scale (integer scale). Rubric scores were resolved by adding the rubric scores assigned by the two human raters, producing a resolved rubric score between 0 and 6. This paper is a continuation of Boulanger and Kumar (2018 , 2019 , 2020) and Kumar and Boulanger (2020) where the objective is to open the AES black box to explain the holistic and rubric scores that it predicts. Essentially, the holistic score ( Boulanger and Kumar, 2018 , 2019 ) is determined and justified through its four rubrics. Rubric scores, in turn, are investigated to highlight the writing features that play an important role within each rubric ( Kumar and Boulanger, 2020 ). Finally, beyond global feature importance, it is not only indispensable to identify which writing indices are important for a particular essay (local), but also to discover how they contribute to increase or decrease the predicted rubric score, and which feature values are more/less desirable ( Boulanger and Kumar, 2020 ). This paper is a continuation of these previous works by adding the following link to the AES chain: holistic score, rubric scores, feature importance, explanations, and formative feedback. The objective is to highlight the means for transparent and trustable AES while empowering learning analytics practitioners with the tools to debug these models and equip educational stakeholders with an AI companion that will semi-autonomously generate formative feedback to teachers and students. Specifically, this paper analyzes the AES reasoning underlying its assessment of the “style” rubric, which looks for command of language, including effective and compelling word choice and varied sentence structure, that clearly supports the writer’s purpose and audience.

This research’s approach to AES leverages a feature-based multi-layer perceptron (MLP) deep neural network to predict rubric scores. The AES system is fed by 1592 linguistic indices quantitatively measured by the Suite of Automatic Linguistic Analysis Tools 2 (SALAT), which assess aspects of grammar and mechanics, sentiment analysis and cognition, text cohesion, lexical diversity, lexical sophistication, and syntactic sophistication and complexity ( Kumar and Boulanger, 2020 ). The purpose of using such a huge pool of low-level writing features is to let deep learning extract the most important ones; the literature supports this practice since there is evidence that features automatically selected are not less interpretable than those engineered ( Woods et al., 2017 ). However, to facilitate this process, this study opted for a semi-automatic strategy that consisted of both filter and embedded methods. Firstly, the original ASAP’s seventh essay dataset consists of a training set of 1567 essays and a validation and testing sets of 894 essays combined. While the texts of all 2461 essays are still available to the public, only the labels (the rubric scores of two human raters) of the training set have been shared with the public. Yet, this paper reused the unlabeled 894 essays of the validation and testing sets for feature selection, a process that must be carefully carried out by avoiding being informed by essays that will train the predictive model. Secondly, feature data were normalized, and features with variances lower than 0.01 were pruned. Thirdly, the last feature of any pair of features having an absolute Pearson correlation coefficient greater than 0.7 was also pruned (the one that comes last in terms of the column ordering in the datasets). After the application of these filter methods, the number of features was reduced from 1592 to 282. Finally, the Lasso and Ridge regression regularization methods (whose combination is also called ElasticNet) were applied during the training of the rubric scoring models. Lasso is responsible for pruning further features, while Ridge regression is entrusted with eliminating multicollinearity among features.

Hyperparameter Optimization and Training

To ensure a fair evaluation of the potential of deep learning, it is of utmost importance to minimally describe this study’s exploration of the hyperparameter space, a step that is often found to be missing when reporting the outcomes of AES models’ performance ( Kumar and Boulanger, 2020 ). First, a study should list the hyperparameters it is going to investigate by testing for various values of each hyperparameter. For example, Table 1 lists all hyperparameters explored in this study. Note that L 1 and L 2 are two regularization hyperparameters contributing to feature selection. Second, each study should also report the range of values of each hyperparameter. Finally, the strategy to explore the selected hyperparameter subspace should be clearly defined. For instance, given the availability of high-performance computing resources and the time/cost of training AES models, one might favor performing a grid (a systematic testing of all combinations of hyperparameters and hyperparameter values within a subspace) or a random search (randomly selecting a hyperparameter value from a range of values per hyperparameter) or both by first applying random search to identify a good starting candidate and then grid search to test all possible combinations in the vicinity of the starting candidate’s subspace. Of particular interest to this study is the neural network itself, that is, how many hidden layers should a neural network have and how many neurons should compose each hidden layer and the neural network as a whole. These two variables are directly related to the size of the neural network, with the number of hidden layers being a defining trait of deep learning. A vast swath of literature is silent about the application of interpretable machine learning in AES and even more about measuring its descriptive accuracy, the two components of trustworthiness. Hence, this study pioneers the comprehensive assessment of deep learning impact on AES’s predictive and descriptive accuracies.

Table 1. Hyperparameter subspace investigated in this article along with best hyperparameter values per neural network architecture.

Consequently, the 1567 labeled essays were divided into a training set (80%) and a testing set (20%). No validation set was put aside; 5-fold cross-validation was rather used for hyperparameter optimization. Table 1 delineates the hyperparameter subspace from which 800 different combinations of hyperparameter values were randomly selected out of a subspace of 86,248,800 possible combinations. Since this research proposes to investigate the potential of deep learning to predict rubric scores, several architectures consisting of 2 to 6 hidden layers and ranging from 9,156 to 119,312 parameters were tested. Table 1 shows the best hyperparameter values per depth of neural networks.

Again, the essays of the testing set were never used during the training and cross-validation processes. In order to retrieve the best predictive models during training, every time the validation loss reached a record low, the model was overwritten. Training stopped when no new record low was reached during 100 epochs. Moreover, to avoid reporting the performance of overfit models, each model was trained five times using the same set of best hyperparameter values. Finally, for each resulting predictive model, a corresponding ensemble model (bagging) was also obtained out of the five models trained during cross-validation.

Predictive Models and Predictive Accuracy

Table 2 delineates the performance of predictive models trained previously by Kumar and Boulanger (2020) on the four scoring rubrics. The first row lists the agreement levels between the resolved and predicted rubric scores measured by the quadratic weighted kappa. The second row is the percentage of accurate predictions; the third row reports the percentages of predictions that are either accurate or off by 1; and the fourth row reports the percentages of predictions that are either accurate or at most off by 2. Prediction of holistic scores is done merely by adding up all rubric scores. Since the scale of rubric scores is 0−6 for every rubric, then the scale of holistic scores is 0−24.

Table 2. Rubric scoring models’ performance on testing set.

While each of these rubric scoring models might suffer from its own systemic bias and hence cancel off each other’s bias by adding up the rubric scores to derive the holistic score, this study (unlike related works) intends to highlight these biases by exposing the decision making process underlying the prediction of rubric scores. Although this paper exclusively focuses on the Style rubric, the methodology put forward to analyze the local and global importance of writing indices and their context-specific contributions to predicted rubric scores is applicable to every rubric and allows to control for these biases one rubric at a time. Comparing and contrasting the role that a specific writing index plays within each rubric context deserves its own investigation, which has been partly addressed in the study led by Kumar and Boulanger (2020) . Moreover, this paper underscores the necessity to measure the predictive accuracy of rubric-based holistic scoring using additional metrics to account for these rubric-specific biases. For example, there exist several combinations of rubric scores to obtain a holistic score of 16 (e.g., 4-4-4-4 vs. 4-3-4-5 vs. 3-5-2-6). Even though the predicted holistic score might be accurate, the rubric scores could all be inaccurate. Similarity or distance metrics (e.g., Manhattan and Euclidean) should then be used to describe the authenticity of the composition of these holistic scores.

According to what Kumar and Boulanger (2020) report on the performance of several state-of-the-art AES systems trained on ASAP’s seventh essay dataset, the AES system they developed and which will be reused in this paper proved competitive while being fully and deeply interpretable, which no other AES system does. They also supply further information about the study setting, essay datasets, rubrics, features, natural language processing (NLP) tools, model training, and evaluation against human performance. Again, this paper showcases the application of explainable artificial intelligence in automated essay scoring by focusing on the decision process of the Rubric #3 (Style) scoring model. Remember that the same methodology is applicable to each rubric.

Explanation Model: SHAP

SH apley A dditive ex P lanations (SHAP) is a theoretically justified XAI framework that can provide simultaneously both local and global explanations ( Molnar, 2020 ); that is, SHAP is able to explain individual predictions taking into account the uniqueness of each prediction, while highlighting the global factors influencing the overall performance of a predictive model. SHAP is of keen interest because it unifies all algorithms of the class of additive feature attribution methods, adhering to a set of three properties that are desirable in interpretable machine learning: local accuracy, missingness, and consistency ( Lundberg and Lee, 2017 ). A key advantage of SHAP is that feature contributions are all expressed in terms of the outcome variable (e.g., rubric scores), providing a same scale to compare the importance of each feature against each other. Local accuracy refers to the fact that no matter the explanation model, the sum of all feature contributions is always equal to the prediction explained by these features. The missingness property implies that the prediction is never explained by unmeasured factors, which are always assigned a contribution of zero. However, the converse is not true; a contribution of zero does not imply an unobserved factor, it can also denote a feature irrelevant to explain the prediction. The consistency property guarantees that a more important feature will always have a greater magnitude than a less important one, no matter how many other features are included in the explanation model. SHAP proves superior to other additive attribution methods such as LIME (Local Interpretable Model-Agnostic Explanations), Shapley values, and DeepLIFT in that they never comply with all three properties, while SHAP does ( Lundberg and Lee, 2017 ). Moreover, the way SHAP assesses the importance of a feature differs from permutation importance methods (e.g., ELI5), measured as the decrease in model performance (accuracy) as a feature is perturbated, in that it is based on how much a feature contributes to every prediction.

Essentially, a SHAP explanation model (linear regression) is trained on top of a predictive model, which in this case is a complex ensemble deep learning model. Table 3 demonstrates a scale explanation model showing how SHAP values (feature contributions) work. In this example, there are five instances and five features describing each instance (in the context of this paper, an instance is an essay). Predictions are listed in the second to last column, and the base value is the mean of all predictions. The base value constitutes the reference point according to which predictions are explained; in other words, reasons are given to justify the discrepancy between the individual prediction and the mean prediction (the base value). Notice that the table does not contain the actual feature values; these are SHAP values that quantify the contribution of each feature to the predicted score. For example, the prediction of Instance 1 is 2.46, while the base value is 3.76. Adding up the feature contributions of Instance 1 to the base value produces the predicted score:

Table 3. Array of SHAP values: local and global importance of features and feature coverage per instance.

Hence, the generic equation of the explanation model ( Lundberg and Lee, 2017 ) is:

where g(x) is the prediction of an individual instance x, σ 0 is the base value, σ i is the feature contribution of feature x i , x i ∈ {0,1} denotes whether feature x i is part of the individual explanation, and j is the total number of features. Furthermore, the global importance of a feature is calculated by adding up the absolute values of its corresponding SHAP values over all instances, where n is the total number of instances and σ i ( j ) is the feature contribution for instance i ( Lundberg et al., 2018 ):

Therefore, it can be seen that Feature 3 is the most globally important feature, while Feature 2 is the least important one. Similarly, Feature 5 is Instance 3’s most important feature at the local level, while Feature 2 is the least locally important. The reader should also note that a feature shall not necessarily be assigned any contribution; some of them are just not part of the explanation such as Feature 2 and Feature 3 in Instance 2. These concepts lay the foundation for the explainable AES system presented in this paper. Just imagine that each instance (essay) will be rather summarized by 282 features and that the explanations of all the testing set’s 314 essays will be provided.

Several implementations of SHAP exist: KernelSHAP, DeepSHAP, GradientSHAP, and TreeSHAP, among others. KernelSHAP is model-agnostic and works for any type of predictive models; however, KernelSHAP is very computing-intensive which makes it undesirable for practical purposes. DeepSHAP and GradientSHAP are two implementations intended for deep learning which takes advantage of the known properties of neural networks (i.e., MLP-NN, CNN, or RNN) to accelerate up to three orders of magnitude the processing time to explain predictions ( Chen et al., 2019 ). Finally, TreeSHAP is the most powerful implementation intended for tree-based models. TreeSHAP is not only fast; it is also accurate. While the three former implementations estimate SHAP values, TreeSHAP computes them exactly. Moreover, TreeSHAP not only measures the contribution of individual features, but it also considers interactions between pairs of features and assigns them SHAP values. Since one of the goals of this paper is to assess the potential of deep learning on the performance of both predictive and explanation models, this research tested the former three implementations. TreeSHAP is recommended for future work since the interaction among features is critical information to consider. Moreover, KernelSHAP, DeepSHAP, and GradientSHAP all require access to the whole original dataset to derive the explanation of a new instance, another constraint TreeSHAP is not subject to.

Descriptive Accuracy: Trustworthiness of Explanation Models

This paper reuses and adapts the methodology introduced by Ribeiro et al. (2016) . Several explanation models will be trained, using different SHAP implementations and configurations, per deep learning predictive model (for each number of hidden layers). The rationale consists in randomly selecting and ignoring 25% of the 282 features feeding the predictive model (e.g., turning them to zero). If it causes the prediction to change beyond a specific threshold (in this study 0.10 and 0.25 were tested), then the explanation model should also reflect the magnitude of this change while ignoring the contributions of these same features. For example, the original predicted rubric score of an essay might be 5; however, when ignoring the information brought in by a subset of 70 randomly selected features (25% of 282), the prediction may turn to 4. On the other side, if the explanation model also predicts a 4 while ignoring the contributions of the same subset of features, then the explanation is considered as trustworthy. This allows to compute the precision, recall, and F1-score of each explanation model (number of true and false positives and true and false negatives). The process is repeated 500 times for every essay to determine the average precision and recall of every explanation model.

Judging Relevancy

So far, the consistency of explanations with predictions has been considered. However, consistent explanations do not imply relevant or meaningful explanations. Put another way, explanations only reflect what predictive models have learned during training. How can the black box of these explanations be opened? Looking directly at the numerical SHAP values of each explanation might seem a daunting task, but there exist tools, mainly visualizations (decision plot, summary plot, and dependence plot), that allow to make sense out of these explanations. However, before visualizing these explanations, another question needs to be addressed: which explanations or essays should be picked for further scrutiny of the AES system? Given the huge number of essays to examine and the tedious task to understand the underpinnings of a single explanation, a small subset of essays should be carefully picked that should represent concisely the state of correctness of the underlying predictive model. Again, this study applies and adapts the methodology in Ribeiro et al. (2016) . A greedy algorithm selects essays whose predictions are explained by as many features of global importance as possible to optimize feature coverage. Ribeiro et al. demonstrated in unrelated studies (i.e., sentiment analysis) that the correctness of a predictive model can be assessed with as few as four or five well-picked explanations.

For example, Table 3 reveals the global importance of five features. The square root of each feature’s global importance is also computed and considered instead to limit the influence of a small group of very influential features. The feature coverage of Instance 1 is 100% because all features are engaged in the explanation of the prediction. On the other hand, Instance 2 has a feature coverage of 61.5% because only Features 1, 4, and 5 are part of the prediction’s explanation. The feature coverage is calculated by summing the square root of each explanation’s feature’s global importance together and dividing by the sum of the square roots of all features’ global importance:

Additionally, it can be seen that Instance 4 does not have any zero-feature value although its feature coverage is only 84.6%. The algorithm was constrained to discard from the explanation any feature whose contribution (local importance) was too close to zero. In the case of Table 3 ’s example, any feature whose absolute SHAP value is less than 0.10 is ignored, hence leading to a feature coverage of:

In this paper’s study, the real threshold was 0.01. This constraint was actually a requirement for the DeepSHAP and GradientSHAP implementations because they only output non-zero SHAP values contrary to KernelSHAP which generates explanations with a fixed number of features: a non-zero SHAP value indicates that the feature is part of the explanation, while a zero value excludes the feature from the explanation. Without this parameter, all 282 features would be part of the explanation although a huge number only has a trivial (very close to zero) SHAP value. Now, a much smaller but variable subset of features makes up each explanation. This is one way in which Ribeiro et al.’s SP-LIME algorithm (SP stands for Submodular Pick) has been adapted to this study’s needs. In conclusion, notice how Instance 4 would be selected in preference to Instance 5 to explain Table 3 ’s underlying predictive model. Even though both instances have four features explaining their prediction, Instance 4’s features are more globally important than Instance 5’s features, and therefore Instance 4 has greater feature coverage than Instance 5.

Whereas Table 3 ’s example exhibits the feature coverage of one instance at a time, this study computes it for a subset of instances, where the absolute SHAP values are aggregated (summed) per candidate subset. When the sum of absolute SHAP values per feature exceeds the set threshold, the feature is then considered as covered by the selected set of instances. The objective in this study was to optimize the feature coverage while minimizing the number of essays to validate the AES model.

Research Questions

One of this article’s objectives is to assess the potential of deep learning in automated essay scoring. The literature has often claimed ( Hussein et al., 2019 ) that there are two approaches to AES, feature-based and deep learning, as though these two approaches were mutually exclusive. Yet, the literature also puts forward that feature-based AES models may be more interpretable than deep learning ones ( Amorim et al., 2018 ). This paper embraces the viewpoint that these two approaches can also be complementary by leveraging the state-of-the-art in NLP and automatic linguistic analysis and harnessing one of the richest pools of linguistic indices put forward in the research community ( Crossley et al., 2016 , 2017 , 2019 ; Kyle, 2016 ; Kyle et al., 2018 ) and applying a thorough feature selection process powered by deep learning. Moreover, the ability of deep learning of modeling complex non-linear relationships makes it particularly well-suited for AES given that the importance of a writing feature is highly dependent on its context, that is, its interactions with other writing features. Besides, this study leverages the SHAP interpretation method that is well-suited to interpret very complex models. Hence, this study elected to work with deep learning models and ensembles to test SHAP’s ability to explain these complex models. Previously, the literature has revealed the difficulty to have at the same time both accurate and interpretable models ( Ribeiro et al., 2016 ; Murdoch et al., 2019 ), where favoring one comes at the expense of the other. However, this research shows how XAI makes it now possible to produce both accurate and interpretable models in the area of AES. Since ensembles have been repeatedly shown to boost the accuracy of predictive models, they were included as part of the tested deep learning architectures to maximize generalizability and accuracy, while making these predictive models interpretable and exploring whether deep learning can even enhance their descriptive accuracy further.

This study investigates the trustworthiness of explanation models, and more specifically, those explaining deep learning predictive models. For instance, does the depth, defined as the number of hidden layers, of an MLP neural network increases the trustworthiness of its SHAP explanation model? The answer to this question will help determine whether it is possible to have very accurate AES models while having competitively interpretable/explainable models, the corner stone for the generation of formative feedback. Remember that formative feedback is defined as “any kind of information provided to students about their actual state of learning or performance in order to modify the learner’s thinking or behavior in the direction of the learning standards” and that formative feedback “conveys where the student is, what are the goals to reach, and how to reach the goals” ( Goldin et al., 2017 ). This notion contrasts with summative feedback which basically is “a justification of the assessment results” ( Hao and Tsikerdekis, 2019 ).

As pointed out in the previous section, multiple SHAP implementations are evaluated in this study. Hence, this paper showcases whether the faster DeepSHAP and GradientSHAP implementations are as reliable as the slower KernelSHAP implementation . The answer to this research question will shed light on the feasibility of providing immediate formative feedback and this multiple times throughout students’ writing processes.

This study also looks at whether a summary of the data produces as trustworthy explanations as those from the original data . This question will be of interest to AES researchers and practitioners because it could allow to significantly decrease the processing time of the computing-intensive and model-agnostic KernelSHAP implementation and test further the potential of customizable explanations.

KernelSHAP allows to specify the total number of features that will shape the explanation of a prediction; for instance, this study experiments with explanations of 16 and 32 features and observes whether there exists a statistically significant difference in the reliability of these explanation models . Knowing this will hint at whether simpler or more complex explanations are more desirable when it comes to optimize their trustworthiness. If there is no statistically significant difference, then AES practitioners are given further flexibility in the selection of SHAP implementations to find the sweet spot between complexity of explanations and speed of processing. For instance, the KernelSHAP implementation allows to customize the number of factors making up an explanation, while the faster DeepSHAP and GradientSHAP do not.

Finally, this paper highlights the means to debug and compare the performance of predictive models through their explanations. Once a model is debugged, the process can be reused to fine-tune feature selection and/or feature engineering to improve predictive models and for the generation of formative feedback to both students and teachers.

The training, validation, and testing sets consist of 1567 essays, each of which has been scored by two human raters, who assigned a score between 0 and 3 per rubric (ideas, organization, style, and conventions). In particular, this article looks at predictive and descriptive accuracy of AES models on the third rubric, style. Note that although each essay has been scored by two human raters, the literature ( Shermis, 2014 ) is not explicit about whether only two or more human raters participated in the scoring of all 1567 essays; given the huge number of essays, it is likely that more than two human raters were involved in the scoring of these essays so that the amount of noise introduced by the various raters’ biases is unknown while probably being at some degree balanced among the two groups of raters. Figure 2 shows the confusion matrices of human raters on Style Rubric. The diagonal elements (dark gray) correspond to exact matches, whereas the light gray squares indicate adjacent matches. Figure 2A delineates the number of essays per pair of ratings, and Figure 2B shows the percentages per pair of ratings. The agreement level between each pair of human raters, measured by the quadratic weighted kappa, is 0.54; the percentage of exact matches is 65.3%; the percentage of adjacent matches is 34.4%; and 0.3% of essays are neither exact nor adjacent matches. Figures 2A,B specify the distributions of 0−3 ratings per group of human raters. Figure 2C exhibits the distribution of resolved scores (a resolved score is the sum of the two human ratings). The mean is 3.99 (with a standard deviation of 1.10), and the median and mode are 4. It is important to note that the levels of predictive accuracy reported in this article are measured on the scale of resolved scores (0−6) and that larger scales tend to slightly inflate quadratic weighted kappa values, which must be taken into account when comparing against the level of agreement between human raters. Comparison of percentages of exact and adjacent matches must also be made with this scoring scale discrepancy in mind.

Figure 2. Summary of the essay dataset (1567 Grade-7 narrative essays) investigated in this study. (A) Number of essays per pair of human ratings; the diagonal (dark gray squares) lists the numbers of exact matches while the light-gray squares list the numbers of adjacent matches; and the bottom row and the rightmost column highlight the distributions of ratings for both groups of human raters. (B) Percentages of essays per pair of human ratings; the diagonal (dark gray squares) lists the percentages of exact matches while the light-gray squares list the percentages of adjacent matches; and the bottom row and the rightmost column highlight the distributions (frequencies) of ratings for both groups of human raters. (C) The distribution of resolved rubric scores; a resolved score is the addition of its two constituent human ratings.

Predictive Accuracy and Descriptive Accuracy

Table 4 compiles the performance outcomes of the 10 predictive models evaluated in this study. The reader should remember that the performance of each model was averaged over five iterations and that two models were trained per number of hidden layers, one non-ensemble and one ensemble. Except for the 6-layer models, there is no clear winner among other models. Even for the 6-layer models, they are superior in terms of exact matches, the primary goal for a reliable AES system, but not according to adjacent matches. Nevertheless, on average ensemble models slightly outperform non-ensemble models. Hence, these ensemble models will be retained for the next analysis step. Moreover, given that five ensemble models were trained per neural network depth, the most accurate model among the five is selected and displayed in Table 4 .

Table 4. Performance of majority classifier and average/maximal performance of trained predictive models.

Next, for each selected ensemble predictive model, several explanation models are trained per predictive model. Every predictive model is explained by the “Deep,” “Grad,” and “Random” explainers, except for the 6-layer model where it was not possible to train a “Deep” explainer apparently due to a bug in the original SHAP code caused by either a unique condition in this study’s data or neural network architecture. However, this was beyond the scope of this study to fix and investigate this issue. As it will be demonstrated, no statistically significant difference exists between the accuracy of these explainers.

The “Random” explainer serves as a baseline model for comparison purpose. Remember that to evaluate the reliability of explanation models, the concurrent impact of randomly selecting and ignoring a subset of features on the prediction and explanation of rubric scores is analyzed. If the prediction changes significantly and its corresponding explanation changes (beyond a set threshold) accordingly (a true positive) or if the prediction remains within the threshold as does the explanation (a true negative), then the explanation is deemed as trustworthy. Hence, in the case of the Random explainer, it simulates random explanations by randomly selecting 32 non-zero features from the original set of 282 features. These random explanations consist only of non-zero features because, according to SHAP’s missingness property, a feature with a zero or a missing value never gets assigned any contribution to the prediction. If at least one of these 32 features is also an element of the subset of the ignored features, then the explanation is considered as untrustworthy, no matter the size of a feature’s contribution.

As for the layer-2 model, six different explanation models are evaluated. Recall that layer-2 models generated the least mean squared error (MSE) during hyperparameter optimization (see Table 1 ). Hence, this specific type of architecture was selected to test the reliability of these various explainers. The “Kernel” explainer is the most computing-intensive and took approximately 8 h of processing. It was trained using the full distributions of feature values in the training set and shaped explanations in terms of 32 features; the “Kernel-16” and “Kernel-32” models were trained on a summary (50 k -means centroids) of the training set to accelerate the processing by about one order of magnitude (less than 1 h). Besides, the “Kernel-16” explainer derived explanations in terms of 16 features, while the “Kernel-32” explainer explained predictions through 32 features. Table 5 exhibits the descriptive accuracy of these various explanation models according to a 0.10 and 0.25 threshold; in other words, by ignoring a subset of randomly picked features, it assesses whether or not the prediction and explanation change simultaneously. Note also how each explanation model, no matter the underlying predictive model, outperforms the “Random” model.

Table 5. Precision, recall, and F1 scores of the various explainers tested per type of predictive model.

The first research question addressed in this subsection asks whether there exists a statistically significant difference between the “Kernel” explainer, which generates 32-feature explanations and is trained on the whole training set, and the “Kernel-32” explainer which also generates 32-feature explanations and is trained on a summary of the training set. To determine this, an independent t-test was conducted using the precision, recall, and F1-score distributions (500 iterations) of both explainers. Table 6 reports the p -values of all the tests and for the 0.10 and 0.25 thresholds. It reveals that there is no statistically significant difference between the two explainers.

Table 6. p -values of independent t -tests comparing whether there exist statistically significant differences between the mean precisions, recalls, and F1-scores of 2-layer explainers and between those of the 2-layer’s, 4-layer’s, and 6-layer’s Gradient explainers.

The next research question tests whether there exists a difference in the trustworthiness of explainers shaping 16 or 32-feature explanations. Again t-tests were conducted to verify this. Table 6 lists the resulting p -values. Again, there is no statistically significant difference in the average precisions, recalls, and F1-scores of both explainers.

This leads to investigating whether the “Kernel,” “Deep,” and “Grad” explainers are equivalent. Table 6 exhibits the results of the t-tests conducted to verify this and reveals that none of the explainers produce a statistically significantly better performance than the other.

Armed with this evidence, it is now possible to verify whether deeper MLP neural networks produce more trustworthy explanation models. For this purpose, the performance of the “Grad” explainer for each type of predictive model will be compared against each other. The same methodology as previously applied is employed here. Table 6 , again, confirms that the explanation model of the 2-layer predictive model is statistically significantly less trustworthy than the 4-layer’s explanation model; the same can be said of the 4-layer and 6-layer models. The only exception is the difference in average precision between 2-layer and 4-layer models and between 4-layer and 6-layer models; however, there clearly exists a statistically significant difference in terms of precision (and also recall and F1-score) between 2-layer and 6-layer models.

The Best Subset of Essays to Judge AES Relevancy

Table 7 lists the four best essays optimizing feature coverage (93.9%) along with their resolved and predicted scores. Notice how two of the four essays were picked by the adapted SP-LIME algorithm with some strong disagreement between the human and the machine graders, two were picked with short and trivial text, and two were picked exhibiting perfect agreement between the human and machine graders. Interestingly, each pair of longer and shorter essays exposes both strong agreement and strong disagreement between the human and AI agents, offering an opportunity to debug the model and evaluate its ability to detect the presence or absence of more basic (e.g., very small number of words, occurrences of sentence fragments) and more advanced aspects (e.g., cohesion between adjacent sentences, variety of sentence structures) of narrative essay writing and to appropriately reward or penalize them.

Table 7. Set of best essays to evaluate the correctness of the 6-layer ensemble AES model.

Local Explanation: The Decision Plot

The decision plot lists writing features by order of importance from top to bottom. The line segments display the contribution (SHAP value) of each feature to the predicted rubric score. Note that an actual decision plot consists of all 282 features and that only the top portion of it (20 most important features) can be displayed (see Figure 3 ). A decision plot is read from bottom to top. The line starts at the base value and ends at the predicted rubric score. Given that the “Grad” explainer is the only explainer common to all predictive models, it has been selected to derive all explanations. The decision plots in Figure 3 show the explanations of the four essays in Table 7 ; the dashed line in these plots represents the explanation of the most accurate predictive model, that is the ensemble model with 6 hidden layers which also produced the most trustworthy explanation model. The predicted rubric score of each explanation model is listed in the bottom-right legend. Explanation of the writing features follow in a next subsection.

Figure 3. Comparisons of all models’ explanations of the most representative set of four essays: (A) Essay 228, (B) Essay 68, (C) Essay 219, and (D) Essay 124.

Global Explanation: The Summary Plot

It is advantageous to use SHAP to build explanation models because it provides a single framework to discover the writing features that are important to an individual essay (local) or a set of essays (global). While the decision plots list features of local importance, Figure 4 ’s summary plot ranks writing features by order of global importance (from top to bottom). All testing set’s 314 essays are represented as dots in the scatterplot of each writing feature. The position of a dot on the horizontal axis corresponds to the importance (SHAP value) of the writing feature for a specific essay and its color indicates the magnitude of the feature value in relation to the range of all 314 feature values. For example, large or small numbers of words within an essay generally contribute to increase or decrease rubric scores by up to 1.5 and 1.0, respectively. Decision plots can also be used to find the most important features for a small subset of essays; Figure 5 demonstrates the new ordering of writing indices when aggregating the feature contributions (summing the absolute values of SHAP values) of the four essays in Table 7 . Moreover, Figure 5 allows to compare the contributions of a feature to various essays. Note how the orderings in Figures 3 −5 can differ from each other, sharing many features of global importance as well as having their own unique features of local importance.

Figure 4. Summary plot listing the 32 most important features globally.

Figure 5. Decision plot delineating the best model’s explanations of Essays 228, 68, 219, and 124 (6-layer ensemble).

Definition of Important Writing Indices

The reader shall understand that it is beyond the scope of this paper to make a thorough description of all writing features. Nevertheless, the summary and decision plots in Figures 4 , 5 allow to identify a subset of features that should be examined in order to validate this study’s predictive model. Supplementary Table 1 combines and describes the 38 features in Figures 4 , 5 .

Dependence Plots

Although the summary plot in Figure 4 is insightful to determine whether small or large feature values are desirable, the dependence plots in Figure 6 prove essential to recommend whether a student should aim at increasing or decreasing the value of a specific writing feature. The dependence plots also reveal whether the student should directly act upon the targeted writing feature or indirectly on other features. The horizontal axis in each of the dependence plots in Figure 6 is the scale of the writing feature and the vertical axis is the scale of the writing feature’s contributions to the predicted rubric scores. Each dot in a dependence plot represents one of the testing set’s 314 essays, that is, the feature value and SHAP value belonging to the essay. The vertical dispersion of the dots on small intervals of the horizontal axis is indicative of interaction with other features ( Molnar, 2020 ). If the vertical dispersion is widespread (e.g., the [50, 100] horizontal-axis interval in the “word_count” dependence plot), then the contribution of the writing feature is most likely at some degree dependent on other writing feature(s).

Figure 6. Dependence plots: the horizontal axes represent feature values while vertical axes represent feature contributions (SHAP values). Each dot represents one of the 314 essays of the testing set and is colored according to the value of the feature with which it interacts most strongly. (A) word_count. (B) hdd42_aw. (C) ncomp_stdev. (D) dobj_per_cl. (E) grammar. (F) SENTENCE_FRAGMENT. (G) Sv_GI. (H) adjacent_overlap_verb_sent.

The contributions of this paper can be summarized as follows: (1) it proposes a means (SHAP) to explain individual predictions of AES systems and provides flexible guidelines to build powerful predictive models using more complex algorithms such as ensembles and deep learning neural networks; (2) it applies a methodology to quantitatively assess the trustworthiness of explanation models; (3) it tests whether faster SHAP implementations impact the descriptive accuracy of explanation models, giving insight on the applicability of SHAP in real pedagogical contexts such as AES; (4) it offers a toolkit to debug AES models, highlights linguistic intricacies, and underscores the means to offer formative feedback to novice writers; and more importantly, (5) it empowers learning analytics practitioners to make AI pedagogical agents accountable to the human educator, the ultimate problem holder responsible for the decisions and actions of AI ( Abbass, 2019 ). Basically, learning analytics (which encompasses tools such as AES) is characterized as an ethics-bound, semi-autonomous, and trust-enabled human-AI fusion that recurrently measures and proactively advances knowledge boundaries in human learning.

To exemplify this, imagine an AES system that supports instructors in the detection of plagiarism, gaming behaviors, and the marking of writing activities. As previously mentioned, essays are marked according to a grid of scoring rubrics: ideas, organization, style, and conventions. While an abundance of data (e.g., the 1592 writing metrics) can be collected by the AES tool, these data might still be insufficient to automate the scoring process of certain rubrics (e.g., ideas). Nevertheless, some scoring subtasks such as assessing a student’s vocabulary, sentence fluency, and conventions might still be assigned to AI since the data types available through existing automatic linguistic analysis tools prove sufficient to reliably alleviate the human marker’s workload. Interestingly, learning analytics is key for the accountability of AI agents to the human problem holder. As the volume of writing data (through a large student population, high-frequency capture of learning episodes, and variety of big learning data) accumulate in the system, new AI agents (predictive models) may apply for the job of “automarker.” These AI agents can be quite transparent through XAI ( Arrieta et al., 2020 ) explanation models, and a human instructor may assess the suitability of an agent for the job and hire the candidate agent that comes closest to human performance. Explanations derived from these models could serve as formative feedback to the students.

The AI marker can be assigned to assess the writing activities that are similar to those previously scored by the human marker(s) from whom it learns. Dissimilar and unseen essays can be automatically assigned to the human marker for reliable scoring, and the AI agent can learn from this manual scoring. To ensure accountability, students should be allowed to appeal the AI agent’s marking to the human marker. In addition, the human marker should be empowered to monitor and validate the scoring of select writing rubrics scored by the AI marker. If the human marker does not agree with the machine scores, the writing assignments may be flagged as incorrectly scored and re-assigned to a human marker. These flagged assignments may serve to update predictive models. Moreover, among the essays that are assigned to the machine marker, a small subset can be simultaneously assigned to the human marker for continuous quality control; that is, to continue comparing whether the agreement level between human and machine markers remains within an acceptable threshold. The human marker should be at any time able to “fire” an AI marker or “hire” an AI marker from a pool of potential machine markers.

This notion of a human-AI fusion has been observed in previous AES systems where the human marker’s workload has been found to be significantly alleviated, passing from scoring several hundreds of essays to just a few dozen ( Dronen et al., 2015 ; Hellman et al., 2019 ). As the AES technology matures and as the learning analytics tools continue to penetrate the education market, this alliance of semi-autonomous human and AI agents will lead to better evidence-based/informed pedagogy ( Nelson and Campbell, 2017 ). Such a human-AI alliance can also be guided to autonomously self-regulate its own hypothesis-authoring and data-acquisition processes for purposes of measuring and advancing knowledge boundaries in human learning.

Real-Time Formative Pedagogical Feedback

This paper provides the evidence that deep learning and SHAP can be used not only to score essays automatically but also to offer explanations in real-time. More specifically, the processing time to derive the 314 explanations of the testing set’s essays has been benchmarked for several types of explainers. It was found that the faster DeepSHAP and GradientSHAP implementations, which took only a few seconds of processing, did not produce less accurate explanations than the much slower KernelSHAP. KernelSHAP took approximately 8 h of processing to derive the explanation model of a 2-layer MLP neural network predictive model and 16 h for the 6-layer predictive model.

This finding also holds for various configurations of KernelSHAP, where the number of features (16 vs. 32) shaping the explanation (where all other features are assigned zero contributions) did not produce a statistically significant difference in the reliability of the explanation models. On average, the models had a precision between 63.9 and 64.1% and a recall between 41.0 and 42.9%. This means that after perturbation of the predictive and explanation models, on average 64% of the predictions the explanation model identified as changing were accurate. On the other side, only about 42% of all predictions that changed were detected by the various 2-layer explainers. An explanation was considered as untrustworthy if the sum of its feature contributions, when added to the average prediction (base value), was not within 0.1 from the perturbated prediction. Similarly, the average precision and recall of 2-layer explainers for the 0.25-threshold were about 69% and 62%, respectively.

Impact of Deep Learning on Descriptive Accuracy of Explanations

By analyzing the performance of the various predictive models in Table 4 , no clear conclusion can be reached as to which model should be deemed as the most desirable. Despite the fact that the 6-layer models slightly outperform the other models in terms of accuracy (percentage of exact matches between the resolved [human] and predicted [machine] scores), they are not the best when it comes to the percentages of adjacent (within 1 and 2) matches. Nevertheless, if the selection of the “best” model is based on the quadratic weighted kappas, the decision remains a nebulous one to make. Moreover, ensuring that machine learning actually learned something meaningful remains paramount, especially in contexts where the performance of a majority classifier is close to the human and machine performance. For example, a majority classifier model would get 46.3% of predictions accurate ( Table 4 ), while trained predictive models at best produce accurate predictions between 51.9 and 55.1%.

Since the interpretability of a machine learning model should be prioritized over accuracy ( Ribeiro et al., 2016 ; Murdoch et al., 2019 ) for questions of transparency and trust, this paper investigated whether the impact of the depth of a MLP neural network might be more visible when assessing its interpretability, that is, the trustworthiness of its corresponding SHAP explanation model. The data in Tables 1 , 5 , 6 effectively support the hypothesis that as the depth of the neural network increases, the precision and recall of the corresponding explanation model improve. Besides, this observation is particularly interesting because the 4-layer (Grad) explainer, which has hardly more parameters than the 2-layer model, is also more accurate than the 2-layer model, suggesting that the 6-layer explainer is most likely superior to other explainers not only because of its greater number of parameters, but also because of its number of hidden layers. By increasing the number of hidden layers, it can be seen that the precision and recall of an explanation model can pass on average from approximately 64 to 73% and from 42 to 52%, respectively, for the 0.10-threshold; and for the 0.25-threshold, from 69 to 79% and from 62 to 75%, respectively.

These results imply that the descriptive accuracy of an explanation model is an evidence of effective machine learning, which may exceed the level of agreement between the human and machine graders. Moreover, given that the superiority of a trained predictive model over a majority classifier is not always obvious, the consistency of its associated explanation model demonstrates this better. Note that theoretically the SHAP explanation model of the majority classifier should assign a zero contribution to each writing feature since the average prediction of such a model is actually the most frequent rubric score given by the human raters; hence, the base value is the explanation.

An interesting fact emerges from Figure 3 , that is, all explainers (2-layer to 6-layer) are more or less similar. It appears that they do not contradict each other. More specifically, they all agree on the direction of the contributions of the most important features. In other words, they unanimously determine that a feature should increase or decrease the predicted score. However, they differ from each other on the magnitude of the feature contributions.

To conclude, this study highlights the need to train predictive models that consider the descriptive accuracy of explanations. The idea is that explanation models consider predictions to derive explanations; explanations should be considered when training predictive models. This would not only help train interpretable models the very first time but also potentially break the status quo that may exist among similar explainers to possibly produce more powerful models. In addition, this research calls for a mechanism (e.g., causal diagrams) to allow teachers to guide the training process of predictive models. Put another way, as LA practitioners debug predictive models, their insights should be encoded in a language that will be understood by the machine and that will guide the training process to avoid learning the same errors and to accelerate the training time.

Accountable AES

Now that the superiority of the 6-layer predictive and explanation models has been demonstrated, some aspects of the relevancy of explanations should be examined more deeply, knowing that having an explanation model consistent with its underlying predictive model does not guarantee relevant explanations. Table 7 discloses the set of four essays that optimize the coverage of most globally important features to evaluate the correctness of the best AES model. It is quite intriguing to note that two of the four essays are among the 16 essays that have a major disagreement (off by 2) between the resolved and predicted rubric scores (1 vs. 3 and 4 vs. 2). The AES tool clearly overrated Essay 228, while it underrated Essay 219. Naturally, these two essays offer an opportunity to understand what is wrong with the model and ultimately debug the model to improve its accuracy and interpretability.

In particular, Essay 228 raises suspicion on the positive contributions of features such as “Ortho_N,” “lemma_mattr,” “all_logical,” “det_pobj_deps_struct,” and “dobj_per_cl.” Moreover, notice how the remaining 262 less important features (not visible in the decision plot in Figure 5 ) have already inflated the rubric score beyond the base value, more than any other essay. Given the very short length and very low quality of the essay, whose meaning is seriously undermined by spelling and grammatical errors, it is of utmost importance to verify how some of these features are computed. For example, is the average number of orthographic neighbors (Ortho_N) per token computed for unmeaningful tokens such as “R” and “whe”? Similarly, are these tokens considered as types in the type-token ratio over lemmas (lemma_mattr)? Given the absence of a meaningful grammatical structure conveying a complete idea through well-articulated words, it becomes obvious that the quality of NLP (natural language processing) parsing may become a source of (measurement) bias impacting both the way some writing features are computed and the predicted rubric score. To remedy this, two solutions are proposed: (1) enhancing the dataset with the part-of-speech sequence or the structure of dependency relationships along with associated confidence levels, or (2) augmenting the essay dataset with essays enclosing various types of non-sensical content to improve the learning of these feature contributions.

Note that all four essays have a text length smaller than the average: 171 words. Notice also how the “hdd42_aw” and “hdd42_fw” play a significant role to decrease the predicted score of Essays 228 and 68. The reader should note that these metrics require a minimum of 42 tokens in order to compute a non-zero D index, a measure of lexical diversity as explained in Supplementary Table 1 . Figure 6B also shows how zero “hdd42_aw” values are heavily penalized. This is extra evidence that supports the strong role that the number of words plays in determining these rubric scores, especially for very short essays where it is one of the few observations that can be reliably recorded.

Two other issues with the best trained AES model were identified. First, in the eyes of the model, the lowest the average number of direct objects per clause (dobj_per_cl), as seen in Figure 6D , the best it is. This appears to contradict one of the requirements of the “Style” rubric, which looks for a variety of sentence structures. Remember that direct objects imply the presence of transitive verbs (action verbs) and that the balanced usage of linking verbs and action verbs as well as of transitive and intransitive verbs is key to meet the requirement of variety of sentence structures. Moreover, note that the writing feature is about counting the number of direct objects per clause, not by sentence. Only one direct object is therefore possible per clause. On the other side, a sentence may contain several clauses, which determines if the sentence is a simple, compound, or a complex sentence. This also means that a sentence may have multiple direct objects and that a high ratio of direct objects per clause is indicative of sentence complexity. Too much complexity is also undesirable. Hence, it is fair to conclude that the higher range of feature values has reasonable feature contributions (SHAP values), while the lower range does not capture well the requirements of the rubric. The dependence plot should rather display a positive peak somewhere in the middle. Notice how the poor quality of Essay 228’s single sentence prevented the proper detection of the single direct object, “broke my finger,” and the so-called absence of direct objects was one of the reasons to wrongfully improve the predicted rubric score.

The model’s second issue discussed here is the presence of sentence fragments, a type of grammatical errors. Essentially, a sentence fragment is a clause that misses one of three critical components: a subject, a verb, or a complete idea. Figure 6E shows the contribution model of grammatical errors, all types combined, while Figure 6F shows specifically the contribution model of sentence fragments. It is interesting to see how SHAP further penalizes larger numbers of grammatical errors and that it takes into account the length of the essay (red dots represent essays with larger numbers of words; blue dots represent essays with smaller numbers of words). For example, except for essays with no identified grammatical errors, longer essays are less penalized than shorter ones. This is particularly obvious when there are 2−4 grammatical errors. The model increases the predicted rubric score only when there is no grammatical error. Moreover, the model tolerates longer essays with only one grammatical error, which sounds quite reasonable. On the other side, the model finds desirable high numbers of sentence fragments, a non-trivial type of grammatical errors. Even worse, the model decreases the rubric score of essays having no sentence fragment. Although grammatical issues are beyond the scope of the “Style” rubric, the model has probably included these features because of their impact on the quality of assessment of vocabulary usage and sentence fluency. The reader should observe how the very poor quality of an essay can even prevent the detection of such fundamental grammatical errors such as in the case of Essay 228, where the AES tool did not find any grammatical error or sentence fragment. Therefore, there should be a way for AES systems to detect a minimum level of text quality before attempting to score an essay. Note that the objective of this section was not to undertake thorough debugging of the model, but rather to underscore the effectiveness of SHAP in doing so.

Formative Feedback

Once an AES model is considered reasonably valid, SHAP can be a suitable formalism to empower the machine to provide formative feedback. For instance, the explanation of Essay 124, which has been assigned a rubric score of 3 by both human and machine markers, indicates that the top two factors contributing to decreasing the predicted rubric score are: (1) the essay length being smaller than average, and (2) the average number of verb lemma types occurring at least once in the next sentence (adjacent_overlap_verb_sent). Figures 6A,H give the overall picture in which the realism of the contributions of these two features can be analyzed. More specifically, Essay 124 is one of very few essays ( Figure 6H ) that makes redundant usage of the same verbs across adjacent sentences. Moreover, the essay displays poor sentence fluency where everything is only expressed in two sentences. To understand more accurately the impact of “adjacent_overlap_verb_sent” on the prediction, a few spelling errors have been corrected and the text has been divided in four sentences instead of two. Revision 1 in Table 8 exhibits the corrections made to the original essay. The decision plot’s dashed line in Figure 3D represents the original explanation of Essay 124, while Figure 7A demonstrates the new explanation of the revised essay. It can be seen that the “adjacent_overlap_verb_sent” feature is still the second most important feature in the new explanation of Essay 124, with a feature value of 0.429, still considered as very poor according to the dependence plot in Figure 6H .

Table 8. Revisions of Essay 124: improvement of sentence splitting, correction of some spelling errors, and elimination of redundant usage of same verbs (bold for emphasis in Essay 124’s original version; corrections in bold for Revisions 1 and 2).

Figure 7. Explanations of the various versions of Essay 124 and evaluation of feature effect for a range of feature values. (A) Explanation of Essay 124’s first revision. (B) Forecasting the effect of changing the ‘adjacent_overlap_verb_sent’ feature on the rubric score. (C) Explanation of Essay 124’s second revision. (D) Comparison of the explanations of all Essay 124’s versions.

To show how SHAP could be leveraged to offer remedial formative feedback, the revised version of Essay 124 will be explained again for eight different values of “adjacent_overlap_verb_sent” (0, 0.143, 0.286, 0.429, 0.571, 0.714, 0.857, 1.0), while keeping the values of all other features constant. The set of these eight essays are explained by a newly trained SHAP explainer (Gradient), producing new SHAP values for each feature and each “revised” essay. Notice how the new model, called the feedback model, allows to foresee by how much a novice writer can hope to improve his/her score according to the “Style” rubric. If the student employs different verbs at every sentence, the feedback model estimates that the rubric score could be improved from 3.47 up to 3.65 ( Figure 7B ). Notice that the dashed line represents Revision 1, while other lines simulate one of the seven other altered essays. Moreover, it is important to note how changing the value of a single feature may influence the contributions that other features may have on the predicted score. Again, all explanations look similar in terms of direction, but certain features differ in terms of the magnitude of their contributions. However, the reader should observe how the targeted feature varies not only in terms of magnitude, but also of direction, allowing the student to ponder the relevancy of executing the recommended writing strategy.

Thus, upon receiving this feedback, assume that a student sets the goal to improve the effectiveness of his/her verb choice by eliminating any redundant verb, producing Revision 2 in Table 8 . The student submits his essay again to the AES system, which finally gives a new rubric score of 3.98, a significant improvement from the previous 3.47, allowing the student to get a 4 instead of a 3. Figure 7C exhibits the decision plot of Revision 2. To better observe how the various revisions of the student’s essay changed over time, their respective explanations have been plotted in the same decision plot ( Figure 7D ). Notice this time that the ordering of the features has changed to list the features of common importance to all of the essay’s versions. The feature ordering in Figures 7A−C complies with the same ordering as in Figure 3D , the decision plot of the original essay. These figures underscore the importance of tracking the interaction between the various features so that the model understands well the impact that changing one feature has on the others. TreeSHAP, an implementation for tree-based models, offers this capability and its potential on improving the quality of feedback provided to novice writers will be tested in a future version of this AES system.

This paper serves as a proof of concept of the applicability of XAI techniques in automated essay scoring, providing learning analytics practitioners and educators with a methodology on how to “hire” AI markers and make them accountable to their human counterparts. In addition to debug predictive models, SHAP explanation models can serve as some formalism of a broader learning analytics platform, where aspects of prescriptive analytics (provision of remedial formative feedback) can be added on top of the more pervasive predictive analytics.

However, the main weakness of the approach put forward in this paper consists in omitting many types of spatio-temporal data. In other words, it ignores precious information inherent to the writing process, which may prove essential to guess the intent of the student, especially in contexts of poor sentence structures and high grammatical inaccuracy. Hence, this paper calls for adapting current NLP technologies to educational purposes, where the quality of writing may be suboptimal, which is contrary to many utopian scenarios where NLP is used for content analysis, opinion mining, topic modeling, or fact extraction trained on corpora of high-quality texts. By capturing the writing process preceding a submission of an essay to an AES tool, other kinds of explanation models can also be trained to offer feedback not only from a linguistic perspective but also from a behavioral one (e.g., composing vs. revising); that is, the AES system could inform novice writers about suboptimal and optimal writing strategies (e.g., planning a revision phase after bursts of writing).

In addition, associating sections of text with suboptimal writing features, those whose contributions lower the predicted score, would be much more informative. This spatial information would not only allow to point out what is wrong and but also where it is wrong, answering more efficiently the question why an essay is wrong. This problem could be simply approached through a multiple-inputs and mixed-data feature-based (MLP) neural network architecture fed by both linguistic indices and textual data ( n -grams), where the SHAP explanation model would assign feature contributions to both types of features and any potential interaction between them. A more complex approach could address the problem through special types of recurrent neural networks such as Ordered-Neurons LSTMs (long short-term memory), which are well adapted to the parsing of natural language, and where the natural sequence of text is not only captured but also its hierarchy of constituents ( Shen et al., 2018 ). After all, this paper highlights the fact that the potential of deep learning can reach beyond the training of powerful predictive models and be better visible in the higher trustworthiness of explanation models. This paper also calls for optimizing the training of predictive models by considering the descriptive accuracy of explanations and the human expert’s qualitative knowledge (e.g., indicating the direction of feature contributions) during the training process.

Data Availability Statement

The datasets and code of this study can be found in these Open Science Framework’s online repositories: https://osf.io/fxvru/ .

Author Contributions

VK architected the concept of an ethics-bound, semi-autonomous, and trust-enabled human-AI fusion that measures and advances knowledge boundaries in human learning, which essentially defines the key traits of learning analytics. DB was responsible for its implementation in the area of explainable automated essay scoring and for the training and validation of the predictive and explanation models. Together they offer an XAI-based proof of concept of a prescriptive model that can offer real-time formative remedial feedback to novice writers. Both authors contributed to the article and approved its publication.

Research reported in this article was supported by the Academic Research Fund (ARF) publication grant of Athabasca University under award number (24087).

Conflict of Interest

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

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2020.572367/full#supplementary-material

• ^ https://www.kaggle.com/c/asap-aes
• ^ https://www.linguisticanalysistools.org/

Abbass, H. A. (2019). Social integration of artificial intelligence: functions, automation allocation logic and human-autonomy trust. Cogn. Comput. 11, 159–171. doi: 10.1007/s12559-018-9619-0

CrossRef Full Text | Google Scholar

Adadi, A., and Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160. doi: 10.1109/ACCESS.2018.2870052

Amorim, E., Cançado, M., and Veloso, A. (2018). “Automated essay scoring in the presence of biased ratings,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , New Orleans, LA, 229–237.

Google Scholar

Arrieta, A. B., Díaz-Rodríguez, N., Ser, J., Del Bennetot, A., Tabik, S., Barbado, A., et al. (2020). Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inform. Fusion 58, 82–115. doi: 10.1016/j.inffus.2019.12.012

Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., et al. (2007). The English lexicon project. Behav. Res. Methods 39, 445–459. doi: 10.3758/BF03193014

PubMed Abstract | CrossRef Full Text | Google Scholar

Boulanger, D., and Kumar, V. (2018). “Deep learning in automated essay scoring,” in Proceedings of the International Conference of Intelligent Tutoring Systems , eds R. Nkambou, R. Azevedo, and J. Vassileva (Cham: Springer International Publishing), 294–299. doi: 10.1007/978-3-319-91464-0_30

Boulanger, D., and Kumar, V. (2019). “Shedding light on the automated essay scoring process,” in Proceedings of the International Conference on Educational Data Mining , 512–515.

Boulanger, D., and Kumar, V. (2020). “SHAPed automated essay scoring: explaining writing features’ contributions to English writing organization,” in Intelligent Tutoring Systems , eds V. Kumar and C. Troussas (Cham: Springer International Publishing), 68–78. doi: 10.1007/978-3-030-49663-0_10

Chen, H., Lundberg, S., and Lee, S.-I. (2019). Explaining models by propagating Shapley values of local components. arXiv [Preprint]. Available online at: https://arxiv.org/abs/1911.11888 (accessed September 22, 2020).

Crossley, S. A., Bradfield, F., and Bustamante, A. (2019). Using human judgments to examine the validity of automated grammar, syntax, and mechanical errors in writing. J. Writ. Res. 11, 251–270. doi: 10.17239/jowr-2019.11.02.01

Crossley, S. A., Kyle, K., and McNamara, D. S. (2016). The tool for the automatic analysis of text cohesion (TAACO): automatic assessment of local, global, and text cohesion. Behav. Res. Methods 48, 1227–1237. doi: 10.3758/s13428-015-0651-7

Crossley, S. A., Kyle, K., and McNamara, D. S. (2017). Sentiment analysis and social cognition engine (SEANCE): an automatic tool for sentiment, social cognition, and social-order analysis. Behav. Res. Methods 49, 803–821. doi: 10.3758/s13428-016-0743-z

Dronen, N., Foltz, P. W., and Habermehl, K. (2015). “Effective sampling for large-scale automated writing evaluation systems,” in Proceedings of the Second (2015) ACM Conference on Learning @ Scale , 3–10.

Goldin, I., Narciss, S., Foltz, P., and Bauer, M. (2017). New directions in formative feedback in interactive learning environments. Int. J. Artif. Intellig. Educ. 27, 385–392. doi: 10.1007/s40593-016-0135-7

Hao, Q., and Tsikerdekis, M. (2019). “How automated feedback is delivered matters: formative feedback and knowledge transfer,” in Proceedings of the 2019 IEEE Frontiers in Education Conference (FIE) , Covington, KY, 1–6.

Hellman, S., Rosenstein, M., Gorman, A., Murray, W., Becker, L., Baikadi, A., et al. (2019). “Scaling up writing in the curriculum: batch mode active learning for automated essay scoring,” in Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale , (New York, NY: Association for Computing Machinery).

Hussein, M. A., Hassan, H., and Nassef, M. (2019). Automated language essay scoring systems: a literature review. PeerJ Comput. Sci. 5:e208. doi: 10.7717/peerj-cs.208

Kumar, V., and Boulanger, D. (2020). Automated essay scoring and the deep learning black box: how are rubric scores determined? Int. J. Artif. Intellig. Educ. doi: 10.1007/s40593-020-00211-5

Kumar, V., Fraser, S. N., and Boulanger, D. (2017). Discovering the predictive power of five baseline writing competences. J. Writ. Anal. 1, 176–226.

Kyle, K. (2016). Measuring Syntactic Development In L2 Writing: Fine Grained Indices Of Syntactic Complexity And Usage-Based Indices Of Syntactic Sophistication. Dissertation, Georgia State University, Atlanta, GA.

Kyle, K., Crossley, S., and Berger, C. (2018). The tool for the automatic analysis of lexical sophistication (TAALES): version 2.0. Behav. Res. Methods 50, 1030–1046. doi: 10.3758/s13428-017-0924-4

Lundberg, S. M., Erion, G. G., and Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. arXiv [Preprint]. Available online at: https://arxiv.org/abs/1802.03888 (accessed September 22, 2020).

Lundberg, S. M., and Lee, S.-I. (2017). “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems , eds I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, et al. (Red Hook, NY: Curran Associates, Inc), 4765–4774.

Madnani, N., and Cahill, A. (2018). “Automated scoring: beyond natural language processing,” in Proceedings of the 27th International Conference on Computational Linguistics , (Santa Fe: Association for Computational Linguistics), 1099–1109.

Madnani, N., Loukina, A., von Davier, A., Burstein, J., and Cahill, A. (2017). “Building better open-source tools to support fairness in automated scoring,” in Proceedings of the First (ACL) Workshop on Ethics in Natural Language Processing , (Valencia: Association for Computational Linguistics), 41–52.

McCarthy, P. M., and Jarvis, S. (2010). MTLD, vocd-D, and HD-D: a validation study of sophisticated approaches to lexical diversity assessment. Behav. Res. Methods 42, 381–392. doi: 10.3758/brm.42.2.381

Mizumoto, T., Ouchi, H., Isobe, Y., Reisert, P., Nagata, R., Sekine, S., et al. (2019). “Analytic score prediction and justification identification in automated short answer scoring,” in Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications , Florence, 316–325.

Molnar, C. (2020). Interpretable Machine Learning . Abu Dhabi: Lulu

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. U.S.A. 116, 22071–22080. doi: 10.1073/pnas.1900654116

Nelson, J., and Campbell, C. (2017). Evidence-informed practice in education: meanings and applications. Educ. Res. 59, 127–135. doi: 10.1080/00131881.2017.1314115

Rahimi, Z., Litman, D., Correnti, R., Wang, E., and Matsumura, L. C. (2017). Assessing students’ use of evidence and organization in response-to-text writing: using natural language processing for rubric-based automated scoring. Int. J. Artif. Intellig. Educ. 27, 694–728. doi: 10.1007/s40593-017-0143-2

Reinertsen, N. (2018). Why can’t it mark this one? A qualitative analysis of student writing rejected by an automated essay scoring system. English Austral. 53:52.

Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). “Why should i trust you?”: explaining the predictions of any classifier. CoRR, abs/1602.0. arXiv [Preprint]. Available online at: http://arxiv.org/abs/1602.04938 (accessed September 22, 2020).

Rupp, A. A. (2018). Designing, evaluating, and deploying automated scoring systems with validity in mind: methodological design decisions. Appl. Meas. Educ. 31, 191–214. doi: 10.1080/08957347.2018.1464448

Rupp, A. A., Casabianca, J. M., Krüger, M., Keller, S., and Köller, O. (2019). Automated essay scoring at scale: a case study in Switzerland and Germany. ETS Res. Rep. Ser. 2019, 1–23. doi: 10.1002/ets2.12249

Shen, Y., Tan, S., Sordoni, A., and Courville, A. C. (2018). Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. CoRR, abs/1810.0. arXiv [Preprint]. Available online at: http://arxiv.org/abs/1810.09536 (accessed September 22, 2020).

Shermis, M. D. (2014). State-of-the-art automated essay scoring: competition, results, and future directions from a United States demonstration. Assess. Writ. 20, 53–76. doi: 10.1016/j.asw.2013.04.001

Taghipour, K. (2017). Robust Trait-Specific Essay Scoring using Neural Networks and Density Estimators. Dissertation, National University of Singapore, Singapore.

West-Smith, P., Butler, S., and Mayfield, E. (2018). “Trustworthy automated essay scoring without explicit construct validity,” in Proceedings of the 2018 AAAI Spring Symposium Series , (New York, NY: ACM).

Woods, B., Adamson, D., Miel, S., and Mayfield, E. (2017). “Formative essay feedback using predictive scoring models,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , (New York, NY: ACM), 2071–2080.

Keywords : explainable artificial intelligence, SHAP, automated essay scoring, deep learning, trust, learning analytics, feedback, rubric

Citation: Kumar V and Boulanger D (2020) Explainable Automated Essay Scoring: Deep Learning Really Has Pedagogical Value. Front. Educ. 5:572367. doi: 10.3389/feduc.2020.572367

Received: 14 June 2020; Accepted: 09 September 2020; Published: 06 October 2020.

Reviewed by:

Copyright © 2020 Kumar and Boulanger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: David Boulanger, [email protected]

This article is part of the Research Topic

Learning Analytics for Supporting Individualization: Data-informed Adaptation of Learning

Automated Essay Grading

A cs109a final project by anmol gupta, annie hwang, paul lisker, and kevin loughlin, introduction.

One of the main responsibilities of teachers and professors in the humanities is grading students essays [1]. Of course, manual essay grading for a classroom of students is a time-consuming process, and can even become tedious at times. Furthermore, essay grading can be plagued by inconsistencies in determining what a “good” essay really is. Indeed, the grading of essays is often a topic of controversy, due to its intrinsic subjectivity. Instructors might be more inclined to better reward essays with a particular voice or writing style, or even a specific position on the essay prompt.

With these and other issues taken into consideration, the problem of essay grading is clearly a field ripe for a more systematic, unbiased method of rating written work. There has been much research into creating AI agents, ultimately based on statistical models, that can automatically grade essays and therefore reduce or even eliminate the potential for bias. Such a model would take typical features of strong essays into account, analyzing each essay for the existence of these features.

In this project, which stems from an existing Kaggle competition sponsored by the William and Flora Hewlett Foundation [2], we have attempted to provide an efficient, automated solution to essay grading, thereby eliminating grader bias, as well as expediting a tedious and time-consuming job. While superior auto-graders that have resulted from years of extensive research surely exist, we feel that our final project demonstrates our ability to apply the data science process learned in this course to a complex, real-world problem.

Data Exploration

It was unnecessary for us to collect any data, as the essays were provided by the Hewlett Foundation. The data is comprised of eight separate essay sets, consisting of a training set of 12,976 essays and a validation set of 4,218 essays. Each essay set had a unique topic and scoring system, which certainly complicated fitting a model, given the diverse data. On the bright side, the essay sets were complete—that is, there was no missing data. Furthermore, the data was clearly laid out in both txt and csv formats that made importing them into a Pandas DataFrame a relatively simple process. In fact, the only complication to arise from collecting the data was a rather sneaky one, only discovered in the later stages when we attempted to spell check the essays. A very small number of essays contained special characters that could not be processed in unicode (the most popular method of text encoding for English). To handle these special characters, we used ISO-8859-1 text encoding, which eliminated encoding-related errors.

The training and validation sets did have plenty of information that we deemed to be extraneous to the scope of our project. For example, the score was often broken down by scorer, and at times into subcategories. We decided to take the average of the overall provided scores as our notion of “score” for each essay. Ultimately, then, the three crucial pieces of information were the essay, the essay set to which it belonged, and the overall essay score.

With the information we needed in place, we tested a few essay features at a basic level to get a better grasp on the data’s format as well as to investigate the sorts of features that might prove useful in predicting an essay’s score. In particular, we calculated the number of words and the vocabulary sizes (number of unique words), for each essay in the training set, plotting them against the provided essay scores.

We hypothesized that word count would certainly be correlated positively with essay score. As students, we note that longer essays often reflect deeper thought and stronger content. On the flip side, there is also value in being succinct by eliminating “filler” content and unnecessary details in papers. As such, we figured that the strength of correlation would weaken as the length of the essays increased.

With regard to vocabulary sizes, we reasoned that individuals who typically read and write more have broader lexicons, and as such a larger vocabulary size would correlate with a higher quality essay, and thus a higher score. After all, the more individuals read and write, the greater their exposure to a larger vocabulary and a more thorough understanding of how properly use it in their own writing. As such, a skilled writer will likely use a variety of exciting words in an effort to more effectively keep readers engaged and to best express their ideas in response to the prompt. Therefore, we hypothesized that a larger vocabulary list would correlate with a higher essay score.

The first notable finding, as evidenced in Figure 1, is that the Word Count vs. Score scatter plots closely mirror the Vocab Size vs Score scatter plots when paired by essay set. This suggests that there might be a relationship between the length of the essay and the different number of words that a writer uses, a discovery that makes sense: a longer essay is bound to have more unique words.

From the Word Counts vs Score scatter plots, we note that in general, there seems to be an upward, positive trend between the essay words counts and the score, with the data expanding in a funnel-like shape. In set 4, there are certainly a couple of essays with the score of 1 that have a smaller word count and vocabulary list than the essays with a score of 0, but that result is likely due to essays with a score of 0 being either incomplete or unrelated to the prompt. As such, these data represent outliers and therefore do not speak to the general, positive relationship. Similar trends and patterns hold true for Vocab Size vs Score.

For set 3, we see that as the scores increase, the range of values for the number of words increases, meaning the number of words themselves tend to increase with score in a tornado-like shape, as mentioned. That is, while low scores were almost exclusively reserved for short essays, good grades were assigned to essays anywhere along the word count spectrum. In other words, there are many essays which have comparable word and vocabulary counts with different scores—especially those of smaller size. On the other hand, those essays with a distinctly greater word count and vocabulary size clearly receive higher scores. Similarly, for sets 1, 2, 4, 5, 6, and 7, we noted that, although the average word count increases as the score increases, the range of word counts also becomes wider, resulting in significant overlap of word counts across scores. This reinforces the conclusion that while word count is, in fact, correlated to essay score, the correlation is weaker for higher-scored essays, since there exists a significant overlap of word count across different scores.

Essay set 8 has different trends: essays with large word counts and vocabulary sizes range greatly in scores. However, despite the unpredictability highlighted by this wide range, a clear predictor does emerge: essays with a small word count and small vocabulary size are graded with correspondingly low scores. As such, unlike in other datasets, where higher word and vocabulary counts equate to higher scores, we see that higher word essays may still be graded across the full range of scores. On the other hand, low word and vocabulary counts are a strong predictor of low score. In our investigation of this phenomenon, we noticed a disparity with essay set 8: it was the only prompt that has a maximum essay length, as measured by word count. Ultimately, this factor could have encouraged essays of particular size, regardless of essay quality.

The Baseline

With a sufficient grasp on the data, we set out to create a baseline essay grading model to which we could compare our final (hopefully more advanced) model. In order to generalize the model across different essay sets (which each contained different scoring systems, as mentioned), we standardized each essay set’s score distribution to have a mean of 0 and a standard deviation of 1.

For the baseline model, we began by considering the various essay features in order to choose the ones that we believed would be most effective, ultimately settling on n-grams. In natural language processing, n-grams are a very powerful resource. An n-gram refers to a consecutive sequence of n words in a given text. As an example, the n-grams with n=1 (unigram) of the sentence “I like ice cream” would be “I”, “like”, “ice”, and “cream”. The bigrams (n=2) of this same sentence would thus be “I like”, “like ice”, and “ice cream”. Ultimately, for a sufficiently large text, n-grams may be analyzed for any positive, nonzero integer n.

In analyzing a text, using n-grams of different n values may be important. For example, while the meaning of “bad” is successfully conveyed as a unigram, it would be lost in “not good,” since the two words would be analyzed independently. In this scenario, then, a bigram would be more useful. By a similar argument, a bigram may be effective for “not good,” but less so for “bad,” since it could associate the word with potentially unrelated words. For our baseline, however, we decided to proceed with unigrams in the name of simplicity.

To quantify the concept of n-grams, we used an information retrieval method called term frequency-inverse document frequency (tf-idf). This measure quantifies the number of times that an n-gram appears in the essay while weighting them based on how frequently the words appear in a general corpus of text. In other words, the tf-idf measure provides a powerful way of standardizing n-gram counts based on the expected number of times that they would have appeared in an essay in the first place. As a result, while the count of a particular n-gram may be large if found often in the text, this can be offset when processed by the tf-idf method if the n-gram is one already frequently appears in essays.

As such, given the benefits of n-grams and their quantification via the tf-idf method, we created a baseline model using unigrams with tf-idf as the predictive features. As our baseline model, we decided to use a simple linear regression model to predict a set of (standardized) scores for our training essays.

To evaluate our linear regression model, we opted to eschew the traditional R^2 measure in favor of Spearman’s Rank Correlation Coefficient. While the traditional R^2 measures determines the accuracy of our model—that is, how closely the predicted scores correspond to the true scores—Spearman instead measures the strength and direction of monotonic association between the essay feature and the score. In other words, it determines how well the ranking of the features corresponds with the ranking of the scores. The benefit of this approach is that this is a useful measure for grading essays, since we're interested to know how directly a feature predicts the relative score of an essay (i.e., how an essay compares to another essay) rather than the actual score given to the essay. Ultimately, this is a better model to measure rather than accuracy, since it gives direct insight into the influence of the feature on the score, and furthermore, because relative accuracy might be more important than actual accuracy.

Spearman results in a score ranging from -1 to 1, where the closer the score is to an absolute value of 1, the stronger the monotonic association (and where positive values imply a positive monotonic association, versus negative values implying a negative one). The closer the value to 0, the weaker the monotonic association. The general consensus of Spearman correlation strength interpretation is as follows:

• .00-.19 “very weak”
• .20-.39 “weak”
• .40-.59 “moderate”
• .60-.79 “strong”
• .80-1.0 “very strong”[3]

As seen in Figure 2, the baseline model received scores that ranged from very weak to moderate, all with p-scores of several factors less than 0.05 (i.e. statistically significant results). However, even with this statistical significance, such weak Spearman correlations are ultimately far too low for this baseline model to provide a trustworthy system. As such, we clearly need a stronger model with a more robust selection of features, as expected!

Advanced Modeling

To improve upon our original model, we first brainstormed what other essay features might better predict an essay’s scores. Our early data exploration pointed to word count and vocab size being useful features. Other trivial features that we opted to include were number of sentences, percent of misspellings, and percentages of each part of speech. We believed these features would be valuable additions to our existing baseline model, as they provide greater insight to the overall structure of each essay, and thus foreseeably could be correlated with score.

However, we also wanted to include at least one nontrivial feature, operating under the belief that essay grading depends on the actual content of the essay—that is, an aspect of the writing that is not captured by trivial statistics on the essay. After all, the number of words in an essay tells us very little about the essay’s content; rather, it is simply generally correlated with better scores. Based on a recommendation by our Teaching Fellow Yoon, we decided to implement the nontrivial perplexity feature.

Perplexity is a measure of the likelihood of a sequence of words of appearing, given a training set of text. Somewhat confusingly, a low perplexity score corresponds to a high likelihood of appearing. As an example, if my training set of three essays were “I like food”, “I like donuts”, and “I like pasta”, the essay “I love pasta” would have a lower perplexity than “you hate cabbage,” since “I love pasta” is more similar to an essay in the training set. This is important, because it gives us a quantifiable way to measure an essay’s content relative to other essays in a set. One would logically conclude that good essays on a certain topic would have similar ideas (and thus similar vocabulary). As such, it follows that given a sufficient training set, perplexity may well provide a valid measure of the content of the essays [4].

Using perplexity proved to be much more of a challenge than anticipated. While the NLTK module provides a method that builds a language model and can subsequently calculate the perplexity of a string based from this model, the method is currently removed from NLTK due to several existing bugs [5]. While alternatives to NLTK do exist, they are all either (a) not free, or (b) generally implemented in C++. Though it is possible to port C++ code into Python, this approach seemed to be time-consuming and beyond the scope of this project. As such, we concluded that the most appealing option was to implement a basic version of the perplexity library ourselves.

We therefore constructed a unigram language model and perplexity function. Ideally, we will be able to expand this functionality to n-grams in the future, but due to time constraints, complexity, code efficiency, and the necessity of testing code we write ourselves, we have only managed to implement perplexity on a unigram model for now. The relationship of each feature to the score can be seen in Figure 3.

Unique word count, word count, and sentence count all seem to have a clearly correlated relationship with score, while perplexity demonstrates a possible trend. It is our belief that with a more advanced perplexity library, perhaps one based on n-grams rather than unigrams, this relationship would be strengthened. Indeed, this is a point of discussion later in this report.

With these these additional features in place, we moved on to select the actual model to predict our response variable. In the end, we decided to continue using linear regression, as we saw no reason to stray from this approach, and also because we were recommended to use such a model! However, we decided that it was important to include a regularization component in order to limit the influence of any collinear relationships among our thousands of features.

We experimented with both Lasso and Ridge regularization, tuning for optimal alpha with values ranging from 0.05 to 1 in 0.05 increments. As learned in class, Lasso performs both parameter shrinkage and variable selection, automatically removing predictors that are collinear with other predictors. Ridge regression, on the other hand, does not zero out coefficients for the predictors, but does minimize them, limiting their effect on the Spearman correlation.

Analysis & Interpretation

With this improved model, we see that the Spearman rank correlations have significantly improved from the baseline model. The Spearman rank correlation values now mostly lie in either the “strong” or “very strong” range, a notable improvement from our baseline model producing mostly “very weak” to “moderate” Spearman values.

In Figure 5, we highlight the scores of the models that yielded the highest Spearman correlations for each of the essay sets. Our highest Spearman correlation was achieved on Essay Set 1, at approximately 0.884, whereas our lowest was achieved on Essay Set 8, at approximately 0.619. It is interesting to note the vast difference in performance across essay sets, a fact that may indicate a failure to sufficiently and successfully generalize the model’s accuracy across such a wide variety of essay sets and prompts. We discuss ways to improve this in the following section.

Figures 4 and 5 also show that Lasso regularization generally performed better than the Ridge regularization, exhibiting better Spearman scores in six out of the eight essay sets; in fact, the average score of Lasso was also slightly higher (.793 as compared to .780). While this difference is not large, we would nonetheless opt for the Lasso model. Given that we have thousands of features with the inclusion of tf-idf, it is likely that plenty of these features are not statistically significant in our linear model. Hence, completely eliminating those features—as Lasso does—rather than just shrinking their coefficients, gives us a more interpretable, computationally efficient, and simpler model.

Ultimately, our Lasso linear regression yielded the greatest overall Spearman correlation, and is intuitively justifiable as a model. With proper tuning for regularization, we note that an alpha value of no greater than 0.5 yielded best results (it should be noted, though, that all nonzero alphas produced comparable Spearman scores, as evidenced in Figure 4). Importantly, p-values remained well below 0.05, confirming the statistical significance of our findings. In layman terms, this high Spearman correlation is significant because it indicates that the scores we have predicted for the essays are relatively similar in rank to the actual scores that the essays have received (as in, if essay A is ranked higher than essay B, our model did well in successfully providing the same conclusion).

Future Work & Concluding Thoughts

In sum, we were able to successfully implement a Lasso linear regression model using both trivial and nontrivial essay features to vastly improve upon our baseline model. While features like word count appear to have the most correlated relationship with score from a graphical standpoint, we believe that a feature such as perplexity, which actually takes a language model into account, would in the long run be a superior predictor. Namely, we would ideally extend our self-implemented perplexity functionality to the n-gram case, rather than simply using unigrams. With this added capability, we believe our model could achieve even greater Spearman correlation scores.

Other features that we believe could improve the effectiveness of the model include parse trees. Parse trees are ordered trees that represent the syntactic structure of a phrase. This linguistic model is, much like perplexity, based on content rather than the “metadata” that many trivial features provide. As such, it may prove effective in contributing to the model a more in-depth analysis of the context and construction of sentences, pointing to writing styles that may correlate to higher grades. Finally, we would like to take the prompts of the essays into account. This could be a significant feature for our model, because depending on the type of essay being writing—e.g. persuasive, narrative, summary—the organization of the essay could vary, which would then affect how we create our models and which features become more important.

There is certainly room for improvement on our model—namely, the features we just mentioned, as well as many more we have not discussed. However, given the time, resources and scope for this project, we were very pleased with our results. None of us had ever performed NLP before, but we now look forward to continuing to apply statistical methodology to such problems in the future!

• U.S. Bureau of Labor Statistics. "What High School Teachers Do." U.S. Bureau of Labor Statistics, Dec. 2015. Web. 13 Dec. 2016. http://www.bls.gov/ooh/education-training-and-library/high-school-teachers.htm#tab-2 .
• The Hewlett Foundation. "The Hewlett Foundation: Automated Essay Scoring." Kaggle, Feb. 2012. Web. 13 Dec. 2016. https://www.kaggle.com/c/asap-aes .
• "Spearman's Correlation." Statstutor, n.d. Web. 14 Dec. 2016. http://www.statstutor.ac.uk/resources/uploaded/spearmans.pdf
• Berwick, Robert C. "Natural Language Processing Notes for Lectures 2 and 3, Fall 2012." Massachusetts Institute of Technology - Natural Language Processing Course. Massachusetts Institute of Technology, n.d. Web. 13 Dec. 2016. http://web.mit.edu/6.863/www/fall2012/lectures/lecture2&3-notes12.pdf .
• "NgramModel No Longer Available? - Issue #738 - Nltk/nltk." GitHub. NLTK Open Source Library, Aug. 2014. Web. 13 Dec. 2016. https://github.com/nltk/nltk/issues/738 .

AI based Automated Essay Grading System using NLP

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An automated essay scoring systems: a systematic literature review

Dadi ramesh.

1 School of Computer Science and Artificial Intelligence, SR University, Warangal, TS India

2 Research Scholar, JNTU, Hyderabad, India

Suresh Kumar Sanampudi

3 Department of Information Technology, JNTUH College of Engineering, Nachupally, Kondagattu, Jagtial, TS India

Associated Data

Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-021-10068-2.

Introduction

Due to COVID 19 outbreak, an online educational system has become inevitable. In the present scenario, almost all the educational institutions ranging from schools to colleges adapt the online education system. The assessment plays a significant role in measuring the learning ability of the student. Most automated evaluation is available for multiple-choice questions, but assessing short and essay answers remain a challenge. The education system is changing its shift to online-mode, like conducting computer-based exams and automatic evaluation. It is a crucial application related to the education domain, which uses natural language processing (NLP) and Machine Learning techniques. The evaluation of essays is impossible with simple programming languages and simple techniques like pattern matching and language processing. Here the problem is for a single question, we will get more responses from students with a different explanation. So, we need to evaluate all the answers concerning the question.

Automated essay scoring (AES) is a computer-based assessment system that automatically scores or grades the student responses by considering appropriate features. The AES research started in 1966 with the Project Essay Grader (PEG) by Ajay et al. ( 1973 ). PEG evaluates the writing characteristics such as grammar, diction, construction, etc., to grade the essay. A modified version of the PEG by Shermis et al. ( 2001 ) was released, which focuses on grammar checking with a correlation between human evaluators and the system. Foltz et al. ( 1999 ) introduced an Intelligent Essay Assessor (IEA) by evaluating content using latent semantic analysis to produce an overall score. Powers et al. ( 2002 ) proposed E-rater and Intellimetric by Rudner et al. ( 2006 ) and Bayesian Essay Test Scoring System (BESTY) by Rudner and Liang ( 2002 ), these systems use natural language processing (NLP) techniques that focus on style and content to obtain the score of an essay. The vast majority of the essay scoring systems in the 1990s followed traditional approaches like pattern matching and a statistical-based approach. Since the last decade, the essay grading systems started using regression-based and natural language processing techniques. AES systems like Dong et al. ( 2017 ) and others developed from 2014 used deep learning techniques, inducing syntactic and semantic features resulting in better results than earlier systems.

Ohio, Utah, and most US states are using AES systems in school education, like Utah compose tool, Ohio standardized test (an updated version of PEG), evaluating millions of student's responses every year. These systems work for both formative, summative assessments and give feedback to students on the essay. Utah provided basic essay evaluation rubrics (six characteristics of essay writing): Development of ideas, organization, style, word choice, sentence fluency, conventions. Educational Testing Service (ETS) has been conducting significant research on AES for more than a decade and designed an algorithm to evaluate essays on different domains and providing an opportunity for test-takers to improve their writing skills. In addition, they are current research content-based evaluation.

The evaluation of essay and short answer scoring should consider the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge. Proper assessment of the parameters mentioned above defines the accuracy of the evaluation system. But all these parameters cannot play an equal role in essay scoring and short answer scoring. In a short answer evaluation, domain knowledge is required, like the meaning of "cell" in physics and biology is different. And while evaluating essays, the implementation of ideas with respect to prompt is required. The system should also assess the completeness of the responses and provide feedback.

Several studies examined AES systems, from the initial to the latest AES systems. In which the following studies on AES systems are Blood ( 2011 ) provided a literature review from PEG 1984–2010. Which has covered only generalized parts of AES systems like ethical aspects, the performance of the systems. Still, they have not covered the implementation part, and it’s not a comparative study and has not discussed the actual challenges of AES systems.

Burrows et al. ( 2015 ) Reviewed AES systems on six dimensions like dataset, NLP techniques, model building, grading models, evaluation, and effectiveness of the model. They have not covered feature extraction techniques and challenges in features extractions. Covered only Machine Learning models but not in detail. This system not covered the comparative analysis of AES systems like feature extraction, model building, and level of relevance, cohesion, and coherence not covered in this review.

Ke et al. ( 2019 ) provided a state of the art of AES system but covered very few papers and not listed all challenges, and no comparative study of the AES model. On the other hand, Hussein et al. in ( 2019 ) studied two categories of AES systems, four papers from handcrafted features for AES systems, and four papers from the neural networks approach, discussed few challenges, and did not cover feature extraction techniques, the performance of AES models in detail.

Klebanov et al. ( 2020 ). Reviewed 50 years of AES systems, listed and categorized all essential features that need to be extracted from essays. But not provided a comparative analysis of all work and not discussed the challenges.

This paper aims to provide a systematic literature review (SLR) on automated essay grading systems. An SLR is an Evidence-based systematic review to summarize the existing research. It critically evaluates and integrates all relevant studies' findings and addresses the research domain's specific research questions. Our research methodology uses guidelines given by Kitchenham et al. ( 2009 ) for conducting the review process; provide a well-defined approach to identify gaps in current research and to suggest further investigation.

We addressed our research method, research questions, and the selection process in Sect.  2 , and the results of the research questions have discussed in Sect.  3 . And the synthesis of all the research questions addressed in Sect.  4 . Conclusion and possible future work discussed in Sect.  5 .

Research method

We framed the research questions with PICOC criteria.

Population (P) Student essays and answers evaluation systems.

Intervention (I) evaluation techniques, data sets, features extraction methods.

Comparison (C) Comparison of various approaches and results.

Outcomes (O) Estimate the accuracy of AES systems,

Context (C) NA.

Research questions

To collect and provide research evidence from the available studies in the domain of automated essay grading, we framed the following research questions (RQ):

RQ1 what are the datasets available for research on automated essay grading?

The answer to the question can provide a list of the available datasets, their domain, and access to the datasets. It also provides a number of essays and corresponding prompts.

RQ2 what are the features extracted for the assessment of essays?

The answer to the question can provide an insight into various features so far extracted, and the libraries used to extract those features.

RQ3, which are the evaluation metrics available for measuring the accuracy of algorithms?

The answer will provide different evaluation metrics for accurate measurement of each Machine Learning approach and commonly used measurement technique.

RQ4 What are the Machine Learning techniques used for automatic essay grading, and how are they implemented?

It can provide insights into various Machine Learning techniques like regression models, classification models, and neural networks for implementing essay grading systems. The response to the question can give us different assessment approaches for automated essay grading systems.

RQ5 What are the challenges/limitations in the current research?

The answer to the question provides limitations of existing research approaches like cohesion, coherence, completeness, and feedback.

Search process

We conducted an automated search on well-known computer science repositories like ACL, ACM, IEEE Explore, Springer, and Science Direct for an SLR. We referred to papers published from 2010 to 2020 as much of the work during these years focused on advanced technologies like deep learning and natural language processing for automated essay grading systems. Also, the availability of free data sets like Kaggle (2012), Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) by Yannakoudakis et al. ( 2011 ) led to research this domain.

Search Strings : We used search strings like “Automated essay grading” OR “Automated essay scoring” OR “short answer scoring systems” OR “essay scoring systems” OR “automatic essay evaluation” and searched on metadata.

Selection criteria

After collecting all relevant documents from the repositories, we prepared selection criteria for inclusion and exclusion of documents. With the inclusion and exclusion criteria, it becomes more feasible for the research to be accurate and specific.

Inclusion criteria 1 Our approach is to work with datasets comprise of essays written in English. We excluded the essays written in other languages.

Inclusion criteria 2  We included the papers implemented on the AI approach and excluded the traditional methods for the review.

Inclusion criteria 3 The study is on essay scoring systems, so we exclusively included the research carried out on only text data sets rather than other datasets like image or speech.

Exclusion criteria  We removed the papers in the form of review papers, survey papers, and state of the art papers.

Quality assessment

In addition to the inclusion and exclusion criteria, we assessed each paper by quality assessment questions to ensure the article's quality. We included the documents that have clearly explained the approach they used, the result analysis and validation.

The quality checklist questions are framed based on the guidelines from Kitchenham et al. ( 2009 ). Each quality assessment question was graded as either 1 or 0. The final score of the study range from 0 to 3. A cut off score for excluding a study from the review is 2 points. Since the papers scored 2 or 3 points are included in the final evaluation. We framed the following quality assessment questions for the final study.

Quality Assessment 1: Internal validity.

Quality Assessment 2: External validity.

Quality Assessment 3: Bias.

The two reviewers review each paper to select the final list of documents. We used the Quadratic Weighted Kappa score to measure the final agreement between the two reviewers. The average resulted from the kappa score is 0.6942, a substantial agreement between the reviewers. The result of evolution criteria shown in Table ​ Table1. 1 . After Quality Assessment, the final list of papers for review is shown in Table ​ Table2. 2 . The complete selection process is shown in Fig. ​ Fig.1. 1 . The total number of selected papers in year wise as shown in Fig. ​ Fig.2. 2 .

Quality assessment analysis

Final list of papers

Selection process

Year wise publications

What are the datasets available for research on automated essay grading?

To work with problem statement especially in Machine Learning and deep learning domain, we require considerable amount of data to train the models. To answer this question, we listed all the data sets used for training and testing for automated essay grading systems. The Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) Yannakoudakis et al. ( 2011 ) developed corpora that contain 1244 essays and ten prompts. This corpus evaluates whether a student can write the relevant English sentences without any grammatical and spelling mistakes. This type of corpus helps to test the models built for GRE and TOFEL type of exams. It gives scores between 1 and 40.

Bailey and Meurers ( 2008 ), Created a dataset (CREE reading comprehension) for language learners and automated short answer scoring systems. The corpus consists of 566 responses from intermediate students. Mohler and Mihalcea ( 2009 ). Created a dataset for the computer science domain consists of 630 responses for data structure assignment questions. The scores are range from 0 to 5 given by two human raters.

Dzikovska et al. ( 2012 ) created a Student Response Analysis (SRA) corpus. It consists of two sub-groups: the BEETLE corpus consists of 56 questions and approximately 3000 responses from students in the electrical and electronics domain. The second one is the SCIENTSBANK(SemEval-2013) (Dzikovska et al. 2013a ; b ) corpus consists of 10,000 responses on 197 prompts on various science domains. The student responses ladled with "correct, partially correct incomplete, Contradictory, Irrelevant, Non-domain."

In the Kaggle (2012) competition, released total 3 types of corpuses on an Automated Student Assessment Prize (ASAP1) (“ https://www.kaggle.com/c/asap-sas/ ” ) essays and short answers. It has nearly 17,450 essays, out of which it provides up to 3000 essays for each prompt. It has eight prompts that test 7th to 10th grade US students. It gives scores between the [0–3] and [0–60] range. The limitations of these corpora are: (1) it has a different score range for other prompts. (2) It uses statistical features such as named entities extraction and lexical features of words to evaluate essays. ASAP +  + is one more dataset from Kaggle. It is with six prompts, and each prompt has more than 1000 responses total of 10,696 from 8th-grade students. Another corpus contains ten prompts from science, English domains and a total of 17,207 responses. Two human graders evaluated all these responses.

Correnti et al. ( 2013 ) created a Response-to-Text Assessment (RTA) dataset used to check student writing skills in all directions like style, mechanism, and organization. 4–8 grade students give the responses to RTA. Basu et al. ( 2013 ) created a power grading dataset with 700 responses for ten different prompts from US immigration exams. It contains all short answers for assessment.

The TOEFL11 corpus Blanchard et al. ( 2013 ) contains 1100 essays evenly distributed over eight prompts. It is used to test the English language skills of a candidate attending the TOFEL exam. It scores the language proficiency of a candidate as low, medium, and high.

International Corpus of Learner English (ICLE) Granger et al. ( 2009 ) built a corpus of 3663 essays covering different dimensions. It has 12 prompts with 1003 essays that test the organizational skill of essay writing, and13 prompts, each with 830 essays that examine the thesis clarity and prompt adherence.

Argument Annotated Essays (AAE) Stab and Gurevych ( 2014 ) developed a corpus that contains 102 essays with 101 prompts taken from the essayforum2 site. It tests the persuasive nature of the student essay. The SCIENTSBANK corpus used by Sakaguchi et al. ( 2015 ) available in git-hub, containing 9804 answers to 197 questions in 15 science domains. Table ​ Table3 3 illustrates all datasets related to AES systems.

ALL types Datasets used in Automatic scoring systems

Features play a major role in the neural network and other supervised Machine Learning approaches. The automatic essay grading systems scores student essays based on different types of features, which play a prominent role in training the models. Based on their syntax and semantics and they are categorized into three groups. 1. statistical-based features Contreras et al. ( 2018 ); Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ) 2. Style-based (Syntax) features Cummins et al. ( 2016 ); Darwish and Mohamed ( 2020 ); Ke et al. ( 2019 ). 3. Content-based features Dong et al. ( 2017 ). A good set of features appropriate models evolved better AES systems. The vast majority of the researchers are using regression models if features are statistical-based. For Neural Networks models, researches are using both style-based and content-based features. The following table shows the list of various features used in existing AES Systems. Table ​ Table4 4 represents all set of features used for essay grading.

Types of features

We studied all the feature extracting NLP libraries as shown in Fig. ​ Fig.3. that 3 . that are used in the papers. The NLTK is an NLP tool used to retrieve statistical features like POS, word count, sentence count, etc. With NLTK, we can miss the essay's semantic features. To find semantic features Word2Vec Mikolov et al. ( 2013 ), GloVe Jeffrey Pennington et al. ( 2014 ) is the most used libraries to retrieve the semantic text from the essays. And in some systems, they directly trained the model with word embeddings to find the score. From Fig. ​ Fig.4 4 as observed that non-content-based feature extraction is higher than content-based.

Usages of tools

Number of papers on content based features

RQ3 which are the evaluation metrics available for measuring the accuracy of algorithms?

The majority of the AES systems are using three evaluation metrics. They are (1) quadrated weighted kappa (QWK) (2) Mean Absolute Error (MAE) (3) Pearson Correlation Coefficient (PCC) Shehab et al. ( 2016 ). The quadratic weighted kappa will find agreement between human evaluation score and system evaluation score and produces value ranging from 0 to 1. And the Mean Absolute Error is the actual difference between human-rated score to system-generated score. The mean square error (MSE) measures the average squares of the errors, i.e., the average squared difference between the human-rated and the system-generated scores. MSE will always give positive numbers only. Pearson's Correlation Coefficient (PCC) finds the correlation coefficient between two variables. It will provide three values (0, 1, − 1). "0" represents human-rated and system scores that are not related. "1" represents an increase in the two scores. "− 1" illustrates a negative relationship between the two scores.

RQ4 what are the Machine Learning techniques being used for automatic essay grading, and how are they implemented?

After scrutinizing all documents, we categorize the techniques used in automated essay grading systems into four baskets. 1. Regression techniques. 2. Classification model. 3. Neural networks. 4. Ontology-based approach.

All the existing AES systems developed in the last ten years employ supervised learning techniques. Researchers using supervised methods viewed the AES system as either regression or classification task. The goal of the regression task is to predict the score of an essay. The classification task is to classify the essays belonging to (low, medium, or highly) relevant to the question's topic. Since the last three years, most AES systems developed made use of the concept of the neural network.

Regression based models

Mohler and Mihalcea ( 2009 ). proposed text-to-text semantic similarity to assign a score to the student essays. There are two text similarity measures like Knowledge-based measures, corpus-based measures. There eight knowledge-based tests with all eight models. They found the similarity. The shortest path similarity determines based on the length, which shortest path between two contexts. Leacock & Chodorow find the similarity based on the shortest path's length between two concepts using node-counting. The Lesk similarity finds the overlap between the corresponding definitions, and Wu & Palmer algorithm finds similarities based on the depth of two given concepts in the wordnet taxonomy. Resnik, Lin, Jiang&Conrath, Hirst& St-Onge find the similarity based on different parameters like the concept, probability, normalization factor, lexical chains. In corpus-based likeness, there LSA BNC, LSA Wikipedia, and ESA Wikipedia, latent semantic analysis is trained on Wikipedia and has excellent domain knowledge. Among all similarity scores, correlation scores LSA Wikipedia scoring accuracy is more. But these similarity measure algorithms are not using NLP concepts. These models are before 2010 and basic concept models to continue the research automated essay grading with updated algorithms on neural networks with content-based features.

Adamson et al. ( 2014 ) proposed an automatic essay grading system which is a statistical-based approach in this they retrieved features like POS, Character count, Word count, Sentence count, Miss spelled words, n-gram representation of words to prepare essay vector. They formed a matrix with these all vectors in that they applied LSA to give a score to each essay. It is a statistical approach that doesn’t consider the semantics of the essay. The accuracy they got when compared to the human rater score with the system is 0.532.

Cummins et al. ( 2016 ). Proposed Timed Aggregate Perceptron vector model to give ranking to all the essays, and later they converted the rank algorithm to predict the score of the essay. The model trained with features like Word unigrams, bigrams, POS, Essay length, grammatical relation, Max word length, sentence length. It is multi-task learning, gives ranking to the essays, and predicts the score for the essay. The performance evaluated through QWK is 0.69, a substantial agreement between the human rater and the system.

Sultan et al. ( 2016 ). Proposed a Ridge regression model to find short answer scoring with Question Demoting. Question Demoting is the new concept included in the essay's final assessment to eliminate duplicate words from the essay. The extracted features are Text Similarity, which is the similarity between the student response and reference answer. Question Demoting is the number of repeats in a student response. With inverse document frequency, they assigned term weight. The sentence length Ratio is the number of words in the student response, is another feature. With these features, the Ridge regression model was used, and the accuracy they got 0.887.

Contreras et al. ( 2018 ). Proposed Ontology based on text mining in this model has given a score for essays in phases. In phase-I, they generated ontologies with ontoGen and SVM to find the concept and similarity in the essay. In phase II from ontologies, they retrieved features like essay length, word counts, correctness, vocabulary, and types of word used, domain information. After retrieving statistical data, they used a linear regression model to find the score of the essay. The accuracy score is the average of 0.5.

Darwish and Mohamed ( 2020 ) proposed the fusion of fuzzy Ontology with LSA. They retrieve two types of features, like syntax features and semantic features. In syntax features, they found Lexical Analysis with tokens, and they construct a parse tree. If the parse tree is broken, the essay is inconsistent—a separate grade assigned to the essay concerning syntax features. The semantic features are like similarity analysis, Spatial Data Analysis. Similarity analysis is to find duplicate sentences—Spatial Data Analysis for finding Euclid distance between the center and part. Later they combine syntax features and morphological features score for the final score. The accuracy they achieved with the multiple linear regression model is 0.77, mostly on statistical features.

Süzen Neslihan et al. ( 2020 ) proposed a text mining approach for short answer grading. First, their comparing model answers with student response by calculating the distance between two sentences. By comparing the model answer with student response, they find the essay's completeness and provide feedback. In this approach, model vocabulary plays a vital role in grading, and with this model vocabulary, the grade will be assigned to the student's response and provides feedback. The correlation between the student answer to model answer is 0.81.

Classification based Models

Persing and Ng ( 2013 ) used a support vector machine to score the essay. The features extracted are OS, N-gram, and semantic text to train the model and identified the keywords from the essay to give the final score.

Sakaguchi et al. ( 2015 ) proposed two methods: response-based and reference-based. In response-based scoring, the extracted features are response length, n-gram model, and syntactic elements to train the support vector regression model. In reference-based scoring, features such as sentence similarity using word2vec is used to find the cosine similarity of the sentences that is the final score of the response. First, the scores were discovered individually and later combined two features to find a final score. This system gave a remarkable increase in performance by combining the scores.

Mathias and Bhattacharyya ( 2018a ; b ) Proposed Automated Essay Grading Dataset with Essay Attribute Scores. The first concept features selection depends on the essay type. So the common attributes are Content, Organization, Word Choice, Sentence Fluency, Conventions. In this system, each attribute is scored individually, with the strength of each attribute identified. The model they used is a random forest classifier to assign scores to individual attributes. The accuracy they got with QWK is 0.74 for prompt 1 of the ASAS dataset ( https://www.kaggle.com/c/asap-sas/ ).

Ke et al. ( 2019 ) used a support vector machine to find the response score. In this method, features like Agreeability, Specificity, Clarity, Relevance to prompt, Conciseness, Eloquence, Confidence, Direction of development, Justification of opinion, and Justification of importance. First, the individual parameter score obtained was later combined with all scores to give a final response score. The features are used in the neural network to find whether the sentence is relevant to the topic or not.

Salim et al. ( 2019 ) proposed an XGBoost Machine Learning classifier to assess the essays. The algorithm trained on features like word count, POS, parse tree depth, and coherence in the articles with sentence similarity percentage; cohesion and coherence are considered for training. And they implemented K-fold cross-validation for a result the average accuracy after specific validations is 68.12.

Neural network models

Shehab et al. ( 2016 ) proposed a neural network method that used learning vector quantization to train human scored essays. After training, the network can provide a score to the ungraded essays. First, we should process the essay to remove Spell checking and then perform preprocessing steps like Document Tokenization, stop word removal, Stemming, and submit it to the neural network. Finally, the model will provide feedback on the essay, whether it is relevant to the topic. And the correlation coefficient between human rater and system score is 0.7665.

Kopparapu and De ( 2016 ) proposed the Automatic Ranking of Essays using Structural and Semantic Features. This approach constructed a super essay with all the responses. Next, ranking for a student essay is done based on the super-essay. The structural and semantic features derived helps to obtain the scores. In a paragraph, 15 Structural features like an average number of sentences, the average length of sentences, and the count of words, nouns, verbs, adjectives, etc., are used to obtain a syntactic score. A similarity score is used as semantic features to calculate the overall score.

Dong and Zhang ( 2016 ) proposed a hierarchical CNN model. The model builds two layers with word embedding to represents the words as the first layer. The second layer is a word convolution layer with max-pooling to find word vectors. The next layer is a sentence-level convolution layer with max-pooling to find the sentence's content and synonyms. A fully connected dense layer produces an output score for an essay. The accuracy with the hierarchical CNN model resulted in an average QWK of 0.754.

Taghipour and Ng ( 2016 ) proposed a first neural approach for essay scoring build in which convolution and recurrent neural network concepts help in scoring an essay. The network uses a lookup table with the one-hot representation of the word vector of an essay. The final efficiency of the network model with LSTM resulted in an average QWK of 0.708.

Dong et al. ( 2017 ). Proposed an Attention-based scoring system with CNN + LSTM to score an essay. For CNN, the input parameters were character embedding and word embedding, and it has attention pooling layers and used NLTK to obtain word and character embedding. The output gives a sentence vector, which provides sentence weight. After CNN, it will have an LSTM layer with an attention pooling layer, and this final layer results in the final score of the responses. The average QWK score is 0.764.

Riordan et al. ( 2017 ) proposed a neural network with CNN and LSTM layers. Word embedding, given as input to a neural network. An LSTM network layer will retrieve the window features and delivers them to the aggregation layer. The aggregation layer is a superficial layer that takes a correct window of words and gives successive layers to predict the answer's sore. The accuracy of the neural network resulted in a QWK of 0.90.

Zhao et al. ( 2017 ) proposed a new concept called Memory-Augmented Neural network with four layers, input representation layer, memory addressing layer, memory reading layer, and output layer. An input layer represents all essays in a vector form based on essay length. After converting the word vector, the memory addressing layer takes a sample of the essay and weighs all the terms. The memory reading layer takes the input from memory addressing segment and finds the content to finalize the score. Finally, the output layer will provide the final score of the essay. The accuracy of essay scores is 0.78, which is far better than the LSTM neural network.

Mathias and Bhattacharyya ( 2018a ; b ) proposed deep learning networks using LSTM with the CNN layer and GloVe pre-trained word embeddings. For this, they retrieved features like Sentence count essays, word count per sentence, Number of OOVs in the sentence, Language model score, and the text's perplexity. The network predicted the goodness scores of each essay. The higher the goodness scores, means higher the rank and vice versa.

Nguyen and Dery ( 2016 ). Proposed Neural Networks for Automated Essay Grading. In this method, a single layer bi-directional LSTM accepting word vector as input. Glove vectors used in this method resulted in an accuracy of 90%.

Ruseti et al. ( 2018 ) proposed a recurrent neural network that is capable of memorizing the text and generate a summary of an essay. The Bi-GRU network with the max-pooling layer molded on the word embedding of each document. It will provide scoring to the essay by comparing it with a summary of the essay from another Bi-GRU network. The result obtained an accuracy of 0.55.

Wang et al. ( 2018a ; b ) proposed an automatic scoring system with the bi-LSTM recurrent neural network model and retrieved the features using the word2vec technique. This method generated word embeddings from the essay words using the skip-gram model. And later, word embedding is used to train the neural network to find the final score. The softmax layer in LSTM obtains the importance of each word. This method used a QWK score of 0.83%.

Dasgupta et al. ( 2018 ) proposed a technique for essay scoring with augmenting textual qualitative Features. It extracted three types of linguistic, cognitive, and psychological features associated with a text document. The linguistic features are Part of Speech (POS), Universal Dependency relations, Structural Well-formedness, Lexical Diversity, Sentence Cohesion, Causality, and Informativeness of the text. The psychological features derived from the Linguistic Information and Word Count (LIWC) tool. They implemented a convolution recurrent neural network that takes input as word embedding and sentence vector, retrieved from the GloVe word vector. And the second layer is the Convolution Layer to find local features. The next layer is the recurrent neural network (LSTM) to find corresponding of the text. The accuracy of this method resulted in an average QWK of 0.764.

Liang et al. ( 2018 ) proposed a symmetrical neural network AES model with Bi-LSTM. They are extracting features from sample essays and student essays and preparing an embedding layer as input. The embedding layer output is transfer to the convolution layer from that LSTM will be trained. Hear the LSRM model has self-features extraction layer, which will find the essay's coherence. The average QWK score of SBLSTMA is 0.801.

Liu et al. ( 2019 ) proposed two-stage learning. In the first stage, they are assigning a score based on semantic data from the essay. The second stage scoring is based on some handcrafted features like grammar correction, essay length, number of sentences, etc. The average score of the two stages is 0.709.

Pedro Uria Rodriguez et al. ( 2019 ) proposed a sequence-to-sequence learning model for automatic essay scoring. They used BERT (Bidirectional Encoder Representations from Transformers), which extracts the semantics from a sentence from both directions. And XLnet sequence to sequence learning model to extract features like the next sentence in an essay. With this pre-trained model, they attained coherence from the essay to give the final score. The average QWK score of the model is 75.5.

Xia et al. ( 2019 ) proposed a two-layer Bi-directional LSTM neural network for the scoring of essays. The features extracted with word2vec to train the LSTM and accuracy of the model in an average of QWK is 0.870.

Kumar et al. ( 2019 ) Proposed an AutoSAS for short answer scoring. It used pre-trained Word2Vec and Doc2Vec models trained on Google News corpus and Wikipedia dump, respectively, to retrieve the features. First, they tagged every word POS and they found weighted words from the response. It also found prompt overlap to observe how the answer is relevant to the topic, and they defined lexical overlaps like noun overlap, argument overlap, and content overlap. This method used some statistical features like word frequency, difficulty, diversity, number of unique words in each response, type-token ratio, statistics of the sentence, word length, and logical operator-based features. This method uses a random forest model to train the dataset. The data set has sample responses with their associated score. The model will retrieve the features from both responses like graded and ungraded short answers with questions. The accuracy of AutoSAS with QWK is 0.78. It will work on any topics like Science, Arts, Biology, and English.

Jiaqi Lun et al. ( 2020 ) proposed an automatic short answer scoring with BERT. In this with a reference answer comparing student responses and assigning scores. The data augmentation is done with a neural network and with one correct answer from the dataset classifying reaming responses as correct or incorrect.

Zhu and Sun ( 2020 ) proposed a multimodal Machine Learning approach for automated essay scoring. First, they count the grammar score with the spaCy library and numerical count as the number of words and sentences with the same library. With this input, they trained a single and Bi LSTM neural network for finding the final score. For the LSTM model, they prepared sentence vectors with GloVe and word embedding with NLTK. Bi-LSTM will check each sentence in both directions to find semantic from the essay. The average QWK score with multiple models is 0.70.

Ontology based approach

Mohler et al. ( 2011 ) proposed a graph-based method to find semantic similarity in short answer scoring. For the ranking of answers, they used the support vector regression model. The bag of words is the main feature extracted in the system.

Ramachandran et al. ( 2015 ) also proposed a graph-based approach to find lexical based semantics. Identified phrase patterns and text patterns are the features to train a random forest regression model to score the essays. The accuracy of the model in a QWK is 0.78.

Zupanc et al. ( 2017 ) proposed sentence similarity networks to find the essay's score. Ajetunmobi and Daramola ( 2017 ) recommended an ontology-based information extraction approach and domain-based ontology to find the score.

Speech response scoring

Automatic scoring is in two ways one is text-based scoring, other is speech-based scoring. This paper discussed text-based scoring and its challenges, and now we cover speech scoring and common points between text and speech-based scoring. Evanini and Wang ( 2013 ), Worked on speech scoring of non-native school students, extracted features with speech ratter, and trained a linear regression model, concluding that accuracy varies based on voice pitching. Loukina et al. ( 2015 ) worked on feature selection from speech data and trained SVM. Malinin et al. ( 2016 ) used neural network models to train the data. Loukina et al. ( 2017 ). Proposed speech and text-based automatic scoring. Extracted text-based features, speech-based features and trained a deep neural network for speech-based scoring. They extracted 33 types of features based on acoustic signals. Malinin et al. ( 2017 ). Wu Xixin et al. ( 2020 ) Worked on deep neural networks for spoken language assessment. Incorporated different types of models and tested them. Ramanarayanan et al. ( 2017 ) worked on feature extraction methods and extracted punctuation, fluency, and stress and trained different Machine Learning models for scoring. Knill et al. ( 2018 ). Worked on Automatic speech recognizer and its errors how its impacts the speech assessment.

The state of the art

This section provides an overview of the existing AES systems with a comparative study w. r. t models, features applied, datasets, and evaluation metrics used for building the automated essay grading systems. We divided all 62 papers into two sets of the first set of review papers in Table ​ Table5 5 with a comparative study of the AES systems.

State of the art

Comparison of all approaches

In our study, we divided major AES approaches into three categories. Regression models, classification models, and neural network models. The regression models failed to find cohesion and coherence from the essay because it trained on BoW(Bag of Words) features. In processing data from input to output, the regression models are less complicated than neural networks. There are unable to find many intricate patterns from the essay and unable to find sentence connectivity. If we train the model with BoW features in the neural network approach, the model never considers the essay's coherence and coherence.

First, to train a Machine Learning algorithm with essays, all the essays are converted to vector form. We can form a vector with BoW and Word2vec, TF-IDF. The BoW and Word2vec vector representation of essays represented in Table ​ Table6. 6 . The vector representation of BoW with TF-IDF is not incorporating the essays semantic, and it’s just statistical learning from a given vector. Word2vec vector comprises semantic of essay in a unidirectional way.

Vector representation of essays

In BoW, the vector contains the frequency of word occurrences in the essay. The vector represents 1 and more based on the happenings of words in the essay and 0 for not present. So, in BoW, the vector does not maintain the relationship with adjacent words; it’s just for single words. In word2vec, the vector represents the relationship between words with other words and sentences prompt in multiple dimensional ways. But word2vec prepares vectors in a unidirectional way, not in a bidirectional way; word2vec fails to find semantic vectors when a word has two meanings, and the meaning depends on adjacent words. Table ​ Table7 7 represents a comparison of Machine Learning models and features extracting methods.

Comparison of models

In AES, cohesion and coherence will check the content of the essay concerning the essay prompt these can be extracted from essay in the vector from. Two more parameters are there to access an essay is completeness and feedback. Completeness will check whether student’s response is sufficient or not though the student wrote correctly. Table ​ Table8 8 represents all four parameters comparison for essay grading. Table ​ Table9 9 illustrates comparison of all approaches based on various features like grammar, spelling, organization of essay, relevance.

Comparison of all models with respect to cohesion, coherence, completeness, feedback

comparison of all approaches on various features

What are the challenges/limitations in the current research?

From our study and results discussed in the previous sections, many researchers worked on automated essay scoring systems with numerous techniques. We have statistical methods, classification methods, and neural network approaches to evaluate the essay automatically. The main goal of the automated essay grading system is to reduce human effort and improve consistency.

The vast majority of essay scoring systems are dealing with the efficiency of the algorithm. But there are many challenges in automated essay grading systems. One should assess the essay by following parameters like the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge.

No model works on the relevance of content, which means whether student response or explanation is relevant to the given prompt or not if it is relevant to how much it is appropriate, and there is no discussion about the cohesion and coherence of the essays. All researches concentrated on extracting the features using some NLP libraries, trained their models, and testing the results. But there is no explanation in the essay evaluation system about consistency and completeness, But Palma and Atkinson ( 2018 ) explained coherence-based essay evaluation. And Zupanc and Bosnic ( 2014 ) also used the word coherence to evaluate essays. And they found consistency with latent semantic analysis (LSA) for finding coherence from essays, but the dictionary meaning of coherence is "The quality of being logical and consistent."

Another limitation is there is no domain knowledge-based evaluation of essays using Machine Learning models. For example, the meaning of a cell is different from biology to physics. Many Machine Learning models extract features with WordVec and GloVec; these NLP libraries cannot convert the words into vectors when they have two or more meanings.

Other challenges that influence the Automated Essay Scoring Systems.

All these approaches worked to improve the QWK score of their models. But QWK will not assess the model in terms of features extraction and constructed irrelevant answers. The QWK is not evaluating models whether the model is correctly assessing the answer or not. There are many challenges concerning students' responses to the Automatic scoring system. Like in evaluating approach, no model has examined how to evaluate the constructed irrelevant and adversarial answers. Especially the black box type of approaches like deep learning models provides more options to the students to bluff the automated scoring systems.

The Machine Learning models that work on statistical features are very vulnerable. Based on Powers et al. ( 2001 ) and Bejar Isaac et al. ( 2014 ), the E-rater was failed on Constructed Irrelevant Responses Strategy (CIRS). From the study of Bejar et al. ( 2013 ), Higgins and Heilman ( 2014 ), observed that when student response contain irrelevant content or shell language concurring to prompt will influence the final score of essays in an automated scoring system.

In deep learning approaches, most of the models automatically read the essay's features, and some methods work on word-based embedding and other character-based embedding features. From the study of Riordan Brain et al. ( 2019 ), The character-based embedding systems do not prioritize spelling correction. However, it is influencing the final score of the essay. From the study of Horbach and Zesch ( 2019 ), Various factors are influencing AES systems. For example, there are data set size, prompt type, answer length, training set, and human scorers for content-based scoring.

Ding et al. ( 2020 ) reviewed that the automated scoring system is vulnerable when a student response contains more words from prompt, like prompt vocabulary repeated in the response. Parekh et al. ( 2020 ) and Kumar et al. ( 2020 ) tested various neural network models of AES by iteratively adding important words, deleting unimportant words, shuffle the words, and repeating sentences in an essay and found that no change in the final score of essays. These neural network models failed to recognize common sense in adversaries' essays and give more options for the students to bluff the automated systems.

Other than NLP and ML techniques for AES. From Wresch ( 1993 ) to Madnani and Cahill ( 2018 ). discussed the complexity of AES systems, standards need to be followed. Like assessment rubrics to test subject knowledge, irrelevant responses, and ethical aspects of an algorithm like measuring the fairness of student response.

Fairness is an essential factor for automated systems. For example, in AES, fairness can be measure in an agreement between human score to machine score. Besides this, From Loukina et al. ( 2019 ), the fairness standards include overall score accuracy, overall score differences, and condition score differences between human and system scores. In addition, scoring different responses in the prospect of constructive relevant and irrelevant will improve fairness.

Madnani et al. ( 2017a ; b ). Discussed the fairness of AES systems for constructed responses and presented RMS open-source tool for detecting biases in the models. With this, one can change fairness standards according to their analysis of fairness.

From Berzak et al.'s ( 2018 ) approach, behavior factors are a significant challenge in automated scoring systems. That helps to find language proficiency, word characteristics (essential words from the text), predict the critical patterns from the text, find related sentences in an essay, and give a more accurate score.

Rupp ( 2018 ), has discussed the designing, evaluating, and deployment methodologies for AES systems. They provided notable characteristics of AES systems for deployment. They are like model performance, evaluation metrics for a model, threshold values, dynamically updated models, and framework.

First, we should check the model performance on different datasets and parameters for operational deployment. Selecting Evaluation metrics for AES models are like QWK, correlation coefficient, or sometimes both. Kelley and Preacher ( 2012 ) have discussed three categories of threshold values: marginal, borderline, and acceptable. The values can be varied based on data size, model performance, type of model (single scoring, multiple scoring models). Once a model is deployed and evaluates millions of responses every time for optimal responses, we need a dynamically updated model based on prompt and data. Finally, framework designing of AES model, hear a framework contains prompts where test-takers can write the responses. One can design two frameworks: a single scoring model for a single methodology and multiple scoring models for multiple concepts. When we deploy multiple scoring models, each prompt could be trained separately, or we can provide generalized models for all prompts with this accuracy may vary, and it is challenging.

Our Systematic literature review on the automated essay grading system first collected 542 papers with selected keywords from various databases. After inclusion and exclusion criteria, we left with 139 articles; on these selected papers, we applied Quality assessment criteria with two reviewers, and finally, we selected 62 writings for final review.

Our observations on automated essay grading systems from 2010 to 2020 are as followed:

• The implementation techniques of automated essay grading systems are classified into four buckets; there are 1. regression models 2. Classification models 3. Neural networks 4. Ontology-based methodology, but using neural networks, the researchers are more accurate than other techniques, and all the methods state of the art provided in Table ​ Table3 3 .
• The majority of the regression and classification models on essay scoring used statistical features to find the final score. It means the systems or models trained on such parameters as word count, sentence count, etc. though the parameters extracted from the essay, the algorithm are not directly training on essays. The algorithms trained on some numbers obtained from the essay and hear if numbers matched the composition will get a good score; otherwise, the rating is less. In these models, the evaluation process is entirely on numbers, irrespective of the essay. So, there is a lot of chance to miss the coherence, relevance of the essay if we train our algorithm on statistical parameters.
• In the neural network approach, the models trained on Bag of Words (BoW) features. The BoW feature is missing the relationship between a word to word and the semantic meaning of the sentence. E.g., Sentence 1: John killed bob. Sentence 2: bob killed John. In these two sentences, the BoW is "John," "killed," "bob."
• In the Word2Vec library, if we are prepared a word vector from an essay in a unidirectional way, the vector will have a dependency with other words and finds the semantic relationship with other words. But if a word has two or more meanings like "Bank loan" and "River Bank," hear bank has two implications, and its adjacent words decide the sentence meaning; in this case, Word2Vec is not finding the real meaning of the word from the sentence.
• The features extracted from essays in the essay scoring system are classified into 3 type's features like statistical features, style-based features, and content-based features, which are explained in RQ2 and Table ​ Table3. 3 . But statistical features, are playing a significant role in some systems and negligible in some systems. In Shehab et al. ( 2016 ); Cummins et al. ( 2016 ). Dong et al. ( 2017 ). Dong and Zhang ( 2016 ). Mathias and Bhattacharyya ( 2018a ; b ) Systems the assessment is entirely on statistical and style-based features they have not retrieved any content-based features. And in other systems that extract content from the essays, the role of statistical features is for only preprocessing essays but not included in the final grading.
• In AES systems, coherence is the main feature to be considered while evaluating essays. The actual meaning of coherence is to stick together. That is the logical connection of sentences (local level coherence) and paragraphs (global level coherence) in a story. Without coherence, all sentences in a paragraph are independent and meaningless. In an Essay, coherence is a significant feature that is explaining everything in a flow and its meaning. It is a powerful feature in AES system to find the semantics of essay. With coherence, one can assess whether all sentences are connected in a flow and all paragraphs are related to justify the prompt. Retrieving the coherence level from an essay is a critical task for all researchers in AES systems.
• In automatic essay grading systems, the assessment of essays concerning content is critical. That will give the actual score for the student. Most of the researches used statistical features like sentence length, word count, number of sentences, etc. But according to collected results, 32% of the systems used content-based features for the essay scoring. Example papers which are on content-based assessment are Taghipour and Ng ( 2016 ); Persing and Ng ( 2013 ); Wang et al. ( 2018a , 2018b ); Zhao et al. ( 2017 ); Kopparapu and De ( 2016 ), Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ); Mohler and Mihalcea ( 2009 ) are used content and statistical-based features. The results are shown in Fig. ​ Fig.3. 3 . And mainly the content-based features extracted with word2vec NLP library, but word2vec is capable of capturing the context of a word in a document, semantic and syntactic similarity, relation with other terms, but word2vec is capable of capturing the context word in a uni-direction either left or right. If a word has multiple meanings, there is a chance of missing the context in the essay. After analyzing all the papers, we found that content-based assessment is a qualitative assessment of essays.
• On the other hand, Horbach and Zesch ( 2019 ); Riordan Brain et al. ( 2019 ); Ding et al. ( 2020 ); Kumar et al. ( 2020 ) proved that neural network models are vulnerable when a student response contains constructed irrelevant, adversarial answers. And a student can easily bluff an automated scoring system by submitting different responses like repeating sentences and repeating prompt words in an essay. From Loukina et al. ( 2019 ), and Madnani et al. ( 2017b ). The fairness of an algorithm is an essential factor to be considered in AES systems.
• While talking about speech assessment, the data set contains audios of duration up to one minute. Feature extraction techniques are entirely different from text assessment, and accuracy varies based on speaking fluency, pitching, male to female voice and boy to adult voice. But the training algorithms are the same for text and speech assessment.
• Once an AES system evaluates essays and short answers accurately in all directions, there is a massive demand for automated systems in the educational and related world. Now AES systems are deployed in GRE, TOEFL exams; other than these, we can deploy AES systems in massive open online courses like Coursera(“ https://coursera.org/learn//machine-learning//exam ”), NPTEL ( https://swayam.gov.in/explorer ), etc. still they are assessing student performance with multiple-choice questions. In another perspective, AES systems can be deployed in information retrieval systems like Quora, stack overflow, etc., to check whether the retrieved response is appropriate to the question or not and can give ranking to the retrieved answers.

Conclusion and future work

As per our Systematic literature review, we studied 62 papers. There exist significant challenges for researchers in implementing automated essay grading systems. Several researchers are working rigorously on building a robust AES system despite its difficulty in solving this problem. All evaluating methods are not evaluated based on coherence, relevance, completeness, feedback, and knowledge-based. And 90% of essay grading systems are used Kaggle ASAP (2012) dataset, which has general essays from students and not required any domain knowledge, so there is a need for domain-specific essay datasets to train and test. Feature extraction is with NLTK, WordVec, and GloVec NLP libraries; these libraries have many limitations while converting a sentence into vector form. Apart from feature extraction and training Machine Learning models, no system is accessing the essay's completeness. No system provides feedback to the student response and not retrieving coherence vectors from the essay—another perspective the constructive irrelevant and adversarial student responses still questioning AES systems.

Our proposed research work will go on the content-based assessment of essays with domain knowledge and find a score for the essays with internal and external consistency. And we will create a new dataset concerning one domain. And another area in which we can improve is the feature extraction techniques.

This study includes only four digital databases for study selection may miss some functional studies on the topic. However, we hope that we covered most of the significant studies as we manually collected some papers published in useful journals.

Below is the link to the electronic supplementary material.

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Contributor Information

Dadi Ramesh, Email: moc.liamg@44hsemaridad .

Suresh Kumar Sanampudi, Email: ni.ca.hutnj@idupmanashserus .

• Adamson, A., Lamb, A., & December, R. M. (2014). Automated Essay Grading.
• Ajay HB, Tillett PI, Page EB (1973) Analysis of essays by computer (AEC-II) (No. 8-0102). Washington, DC: U.S. Department of Health, Education, and Welfare, Office of Education, National Center for Educational Research and Development
• Ajetunmobi SA, Daramola O (2017) Ontology-based information extraction for subject-focussed automatic essay evaluation. In: 2017 International Conference on Computing Networking and Informatics (ICCNI) p 1–6. IEEE
• Alva-Manchego F, et al. (2019) EASSE: Easier Automatic Sentence Simplification Evaluation.” ArXiv abs/1908.04567 (2019): n. pag
• Bailey S, Meurers D (2008) Diagnosing meaning errors in short answers to reading comprehension questions. In: Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications (Columbus), p 107–115
• Basu S, Jacobs C, Vanderwende L. Powergrading: a clustering approach to amplify human effort for short answer grading. Trans Assoc Comput Linguist (TACL) 2013; 1 :391–402. doi: 10.1162/tacl_a_00236. [ CrossRef ] [ Google Scholar ]
• Bejar, I. I., Flor, M., Futagi, Y., & Ramineni, C. (2014). On the vulnerability of automated scoring to construct-irrelevant response strategies (CIRS): An illustration. Assessing Writing, 22, 48-59.
• Bejar I, et al. (2013) Length of Textual Response as a Construct-Irrelevant Response Strategy: The Case of Shell Language. Research Report. ETS RR-13-07.” ETS Research Report Series (2013): n. pag
• Berzak Y, et al. (2018) “Assessing Language Proficiency from Eye Movements in Reading.” ArXiv abs/1804.07329 (2018): n. pag
• Blanchard D, Tetreault J, Higgins D, Cahill A, Chodorow M (2013) TOEFL11: A corpus of non-native English. ETS Research Report Series, 2013(2):i–15, 2013
• Blood, I. (2011). Automated essay scoring: a literature review. Studies in Applied Linguistics and TESOL, 11(2).
• Burrows S, Gurevych I, Stein B. The eras and trends of automatic short answer grading. Int J Artif Intell Educ. 2015; 25 :60–117. doi: 10.1007/s40593-014-0026-8. [ CrossRef ] [ Google Scholar ]
• Cader, A. (2020, July). The Potential for the Use of Deep Neural Networks in e-Learning Student Evaluation with New Data Augmentation Method. In International Conference on Artificial Intelligence in Education (pp. 37–42). Springer, Cham.
• Cai C (2019) Automatic essay scoring with recurrent neural network. In: Proceedings of the 3rd International Conference on High Performance Compilation, Computing and Communications (2019): n. pag.
• Chen M, Li X (2018) "Relevance-Based Automated Essay Scoring via Hierarchical Recurrent Model. In: 2018 International Conference on Asian Language Processing (IALP), Bandung, Indonesia, 2018, p 378–383, doi: 10.1109/IALP.2018.8629256
• Chen Z, Zhou Y (2019) "Research on Automatic Essay Scoring of Composition Based on CNN and OR. In: 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, p 13–18, doi: 10.1109/ICAIBD.2019.8837007
• Contreras JO, Hilles SM, Abubakar ZB (2018) Automated essay scoring with ontology based on text mining and NLTK tools. In: 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 1-6
• Correnti R, Matsumura LC, Hamilton L, Wang E. Assessing students’ skills at writing analytically in response to texts. Elem Sch J. 2013; 114 (2):142–177. doi: 10.1086/671936. [ CrossRef ] [ Google Scholar ]
• Cummins, R., Zhang, M., & Briscoe, E. (2016, August). Constrained multi-task learning for automated essay scoring. Association for Computational Linguistics.
• Darwish SM, Mohamed SK (2020) Automated essay evaluation based on fusion of fuzzy ontology and latent semantic analysis. In: Hassanien A, Azar A, Gaber T, Bhatnagar RF, Tolba M (eds) The International Conference on Advanced Machine Learning Technologies and Applications
• Dasgupta T, Naskar A, Dey L, Saha R (2018) Augmenting textual qualitative features in deep convolution recurrent neural network for automatic essay scoring. In: Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications p 93–102
• Ding Y, et al. (2020) "Don’t take “nswvtnvakgxpm” for an answer–The surprising vulnerability of automatic content scoring systems to adversarial input." In: Proceedings of the 28th International Conference on Computational Linguistics
• Dong F, Zhang Y (2016) Automatic features for essay scoring–an empirical study. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing p 1072–1077
• Dong F, Zhang Y, Yang J (2017) Attention-based recurrent convolutional neural network for automatic essay scoring. In: Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) p 153–162
• Dzikovska M, Nielsen R, Brew C, Leacock C, Gi ampiccolo D, Bentivogli L, Clark P, Dagan I, Dang HT (2013a) Semeval-2013 task 7: The joint student response analysis and 8th recognizing textual entailment challenge
• Dzikovska MO, Nielsen R, Brew C, Leacock C, Giampiccolo D, Bentivogli L, Clark P, Dagan I, Trang Dang H (2013b) SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge. *SEM 2013: The First Joint Conference on Lexical and Computational Semantics
• Educational Testing Service (2008) CriterionSM online writing evaluation service. Retrieved from http://www.ets.org/s/criterion/pdf/9286_CriterionBrochure.pdf .
• Evanini, K., & Wang, X. (2013, August). Automated speech scoring for non-native middle school students with multiple task types. In INTERSPEECH (pp. 2435–2439).
• Foltz PW, Laham D, Landauer TK (1999) The Intelligent Essay Assessor: Applications to Educational Technology. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 1, 2, http://imej.wfu.edu/articles/1999/2/04/ index.asp
• Granger, S., Dagneaux, E., Meunier, F., & Paquot, M. (Eds.). (2009). International corpus of learner English. Louvain-la-Neuve: Presses universitaires de Louvain.
• Higgins D, Heilman M. Managing what we can measure: quantifying the susceptibility of automated scoring systems to gaming behavior” Educ Meas Issues Pract. 2014; 33 :36–46. doi: 10.1111/emip.12036. [ CrossRef ] [ Google Scholar ]
• Horbach A, Zesch T. The influence of variance in learner answers on automatic content scoring. Front Educ. 2019; 4 :28. doi: 10.3389/feduc.2019.00028. [ CrossRef ] [ Google Scholar ]
• https://www.coursera.org/learn/machine-learning/exam/7pytE/linear-regression-with-multiple-variables/attempt
• Hussein, M. A., Hassan, H., & Nassef, M. (2019). Automated language essay scoring systems: A literature review. PeerJ Computer Science, 5, e208. [ PMC free article ] [ PubMed ]
• Ke Z, Ng V (2019) “Automated essay scoring: a survey of the state of the art.” IJCAI
• Ke, Z., Inamdar, H., Lin, H., & Ng, V. (2019, July). Give me more feedback II: Annotating thesis strength and related attributes in student essays. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3994-4004).
• Kelley K, Preacher KJ. On effect size. Psychol Methods. 2012; 17 (2):137–152. doi: 10.1037/a0028086. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kitchenham B, Brereton OP, Budgen D, Turner M, Bailey J, Linkman S. Systematic literature reviews in software engineering–a systematic literature review. Inf Softw Technol. 2009; 51 (1):7–15. doi: 10.1016/j.infsof.2008.09.009. [ CrossRef ] [ Google Scholar ]
• Klebanov, B. B., & Madnani, N. (2020, July). Automated evaluation of writing–50 years and counting. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7796–7810).
• Knill K, Gales M, Kyriakopoulos K, et al. (4 more authors) (2018) Impact of ASR performance on free speaking language assessment. In: Interspeech 2018.02–06 Sep 2018, Hyderabad, India. International Speech Communication Association (ISCA)
• Kopparapu SK, De A (2016) Automatic ranking of essays using structural and semantic features. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), p 519–523
• Kumar, Y., Aggarwal, S., Mahata, D., Shah, R. R., Kumaraguru, P., & Zimmermann, R. (2019, July). Get it scored using autosas—an automated system for scoring short answers. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 9662–9669).
• Kumar Y, et al. (2020) “Calling out bluff: attacking the robustness of automatic scoring systems with simple adversarial testing.” ArXiv abs/2007.06796
• Li X, Chen M, Nie J, Liu Z, Feng Z, Cai Y (2018) Coherence-Based Automated Essay Scoring Using Self-attention. In: Sun M, Liu T, Wang X, Liu Z, Liu Y (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL 2018, NLP-NABD 2018. Lecture Notes in Computer Science, vol 11221. Springer, Cham. 10.1007/978-3-030-01716-3_32
• Liang G, On B, Jeong D, Kim H, Choi G. Automated essay scoring: a siamese bidirectional LSTM neural network architecture. Symmetry. 2018; 10 :682. doi: 10.3390/sym10120682. [ CrossRef ] [ Google Scholar ]
• Liua, H., Yeb, Y., & Wu, M. (2018, April). Ensemble Learning on Scoring Student Essay. In 2018 International Conference on Management and Education, Humanities and Social Sciences (MEHSS 2018). Atlantis Press.
• Liu J, Xu Y, Zhao L (2019) Automated Essay Scoring based on Two-Stage Learning. ArXiv, abs/1901.07744
• Loukina A, et al. (2015) Feature selection for automated speech scoring.” BEA@NAACL-HLT
• Loukina A, et al. (2017) “Speech- and Text-driven Features for Automated Scoring of English-Speaking Tasks.” SCNLP@EMNLP 2017
• Loukina A, et al. (2019) The many dimensions of algorithmic fairness in educational applications. BEA@ACL
• Lun J, Zhu J, Tang Y, Yang M (2020) Multiple data augmentation strategies for improving performance on automatic short answer scoring. In: Proceedings of the AAAI Conference on Artificial Intelligence, 34(09): 13389-13396
• Madnani, N., & Cahill, A. (2018, August). Automated scoring: Beyond natural language processing. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 1099–1109).
• Madnani N, et al. (2017b) “Building better open-source tools to support fairness in automated scoring.” EthNLP@EACL
• Malinin A, et al. (2016) “Off-topic response detection for spontaneous spoken english assessment.” ACL
• Malinin A, et al. (2017) “Incorporating uncertainty into deep learning for spoken language assessment.” ACL
• Mathias S, Bhattacharyya P (2018a) Thank “Goodness”! A Way to Measure Style in Student Essays. In: Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications p 35–41
• Mathias S, Bhattacharyya P (2018b) ASAP++: Enriching the ASAP automated essay grading dataset with essay attribute scores. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
• Mikolov T, et al. (2013) “Efficient Estimation of Word Representations in Vector Space.” ICLR
• Mohler M, Mihalcea R (2009) Text-to-text semantic similarity for automatic short answer grading. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009) p 567–575
• Mohler M, Bunescu R, Mihalcea R (2011) Learning to grade short answer questions using semantic similarity measures and dependency graph alignments. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies p 752–762
• Muangkammuen P, Fukumoto F (2020) Multi-task Learning for Automated Essay Scoring with Sentiment Analysis. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop p 116–123
• Nguyen, H., & Dery, L. (2016). Neural networks for automated essay grading. CS224d Stanford Reports, 1–11.
• Palma D, Atkinson J. Coherence-based automatic essay assessment. IEEE Intell Syst. 2018; 33 (5):26–36. doi: 10.1109/MIS.2018.2877278. [ CrossRef ] [ Google Scholar ]
• Parekh S, et al (2020) My Teacher Thinks the World Is Flat! Interpreting Automatic Essay Scoring Mechanism.” ArXiv abs/2012.13872 (2020): n. pag
• Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543).
• Persing I, Ng V (2013) Modeling thesis clarity in student essays. In:Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) p 260–269
• Powers DE, Burstein JC, Chodorow M, Fowles ME, Kukich K. Stumping E-Rater: challenging the validity of automated essay scoring. ETS Res Rep Ser. 2001; 2001 (1):i–44. [ Google Scholar ]
• Powers DE, Burstein JC, Chodorow M, Fowles ME, Kukich K. Stumping e-rater: challenging the validity of automated essay scoring. Comput Hum Behav. 2002; 18 (2):103–134. doi: 10.1016/S0747-5632(01)00052-8. [ CrossRef ] [ Google Scholar ]
• Ramachandran L, Cheng J, Foltz P (2015) Identifying patterns for short answer scoring using graph-based lexico-semantic text matching. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications p 97–106
• Ramanarayanan V, et al. (2017) “Human and Automated Scoring of Fluency, Pronunciation and Intonation During Human-Machine Spoken Dialog Interactions.” INTERSPEECH
• Riordan B, Horbach A, Cahill A, Zesch T, Lee C (2017) Investigating neural architectures for short answer scoring. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications p 159–168
• Riordan B, Flor M, Pugh R (2019) "How to account for misspellings: Quantifying the benefit of character representations in neural content scoring models."In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
• Rodriguez P, Jafari A, Ormerod CM (2019) Language models and Automated Essay Scoring. ArXiv, abs/1909.09482
• Rudner, L. M., & Liang, T. (2002). Automated essay scoring using Bayes' theorem. The Journal of Technology, Learning and Assessment, 1(2).
• Rudner, L. M., Garcia, V., & Welch, C. (2006). An evaluation of IntelliMetric™ essay scoring system. The Journal of Technology, Learning and Assessment, 4(4).
• Rupp A. Designing, evaluating, and deploying automated scoring systems with validity in mind: methodological design decisions. Appl Meas Educ. 2018; 31 :191–214. doi: 10.1080/08957347.2018.1464448. [ CrossRef ] [ Google Scholar ]
• Ruseti S, Dascalu M, Johnson AM, McNamara DS, Balyan R, McCarthy KS, Trausan-Matu S (2018) Scoring summaries using recurrent neural networks. In: International Conference on Intelligent Tutoring Systems p 191–201. Springer, Cham
• Sakaguchi K, Heilman M, Madnani N (2015) Effective feature integration for automated short answer scoring. In: Proceedings of the 2015 conference of the North American Chapter of the association for computational linguistics: Human language technologies p 1049–1054
• Salim, Y., Stevanus, V., Barlian, E., Sari, A. C., & Suhartono, D. (2019, December). Automated English Digital Essay Grader Using Machine Learning. In 2019 IEEE International Conference on Engineering, Technology and Education (TALE) (pp. 1–6). IEEE.
• Shehab A, Elhoseny M, Hassanien AE (2016) A hybrid scheme for Automated Essay Grading based on LVQ and NLP techniques. In: 12th International Computer Engineering Conference (ICENCO), Cairo, 2016, p 65-70
• Shermis MD, Mzumara HR, Olson J, Harrington S. On-line grading of student essays: PEG goes on the World Wide Web. Assess Eval High Educ. 2001; 26 (3):247–259. doi: 10.1080/02602930120052404. [ CrossRef ] [ Google Scholar ]
• Stab C, Gurevych I (2014) Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) p 46–56
• Sultan MA, Salazar C, Sumner T (2016) Fast and easy short answer grading with high accuracy. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies p 1070–1075
• Süzen, N., Gorban, A. N., Levesley, J., & Mirkes, E. M. (2020). Automatic short answer grading and feedback using text mining methods. Procedia Computer Science, 169, 726–743.
• Taghipour K, Ng HT (2016) A neural approach to automated essay scoring. In: Proceedings of the 2016 conference on empirical methods in natural language processing p 1882–1891
• Tashu TM (2020) "Off-Topic Essay Detection Using C-BGRU Siamese. In: 2020 IEEE 14th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, p 221–225, doi: 10.1109/ICSC.2020.00046
• Tashu TM, Horváth T (2019) A layered approach to automatic essay evaluation using word-embedding. In: McLaren B, Reilly R, Zvacek S, Uhomoibhi J (eds) Computer Supported Education. CSEDU 2018. Communications in Computer and Information Science, vol 1022. Springer, Cham
• Tashu TM, Horváth T (2020) Semantic-Based Feedback Recommendation for Automatic Essay Evaluation. In: Bi Y, Bhatia R, Kapoor S (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham
• Uto M, Okano M (2020) Robust Neural Automated Essay Scoring Using Item Response Theory. In: Bittencourt I, Cukurova M, Muldner K, Luckin R, Millán E (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham
• Wang Z, Liu J, Dong R (2018a) Intelligent Auto-grading System. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) p 430–435. IEEE.
• Wang Y, et al. (2018b) “Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning.” EMNLP
• Zhu W, Sun Y (2020) Automated essay scoring system using multi-model Machine Learning, david c. wyld et al. (eds): mlnlp, bdiot, itccma, csity, dtmn, aifz, sigpro
• Wresch W. The Imminence of Grading Essays by Computer-25 Years Later. Comput Compos. 1993; 10 :45–58. doi: 10.1016/S8755-4615(05)80058-1. [ CrossRef ] [ Google Scholar ]
• Wu, X., Knill, K., Gales, M., & Malinin, A. (2020). Ensemble approaches for uncertainty in spoken language assessment.
• Xia L, Liu J, Zhang Z (2019) Automatic Essay Scoring Model Based on Two-Layer Bi-directional Long-Short Term Memory Network. In: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence p 133–137
• Yannakoudakis H, Briscoe T, Medlock B (2011) A new dataset and method for automatically grading ESOL texts. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies p 180–189
• Zhao S, Zhang Y, Xiong X, Botelho A, Heffernan N (2017) A memory-augmented neural model for automated grading. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale p 189–192
• Zupanc K, Bosnic Z (2014) Automated essay evaluation augmented with semantic coherence measures. In: 2014 IEEE International Conference on Data Mining p 1133–1138. IEEE.
• Zupanc K, Savić M, Bosnić Z, Ivanović M (2017) Evaluating coherence of essays using sentence-similarity networks. In: Proceedings of the 18th International Conference on Computer Systems and Technologies p 65–72
• Dzikovska, M. O., Nielsen, R., & Brew, C. (2012, June). Towards effective tutorial feedback for explanation questions: A dataset and baselines. In  Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  (pp. 200-210).
• Kumar, N., & Dey, L. (2013, November). Automatic Quality Assessment of documents with application to essay grading. In 2013 12th Mexican International Conference on Artificial Intelligence (pp. 216–222). IEEE.
• Wu, S. H., & Shih, W. F. (2018, July). A short answer grading system in chinese by support vector approach. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications (pp. 125-129).
• Agung Putri Ratna, A., Lalita Luhurkinanti, D., Ibrahim I., Husna D., Dewi Purnamasari P. (2018). Automatic Essay Grading System for Japanese Language Examination Using Winnowing Algorithm, 2018 International Seminar on Application for Technology of Information and Communication, 2018, pp. 565–569. 10.1109/ISEMANTIC.2018.8549789.
• Sharma A., & Jayagopi D. B. (2018). Automated Grading of Handwritten Essays 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018, pp 279–284. 10.1109/ICFHR-2018.2018.00056

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PMAES : Prompt-mapping Contrastive Learning for Cross-prompt Automated Essay Scoring

Yuan Chen , Xia Li

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• PMAES: Prompt-mapping Contrastive Learning for Cross-prompt Automated Essay Scoring (Chen & Li, ACL 2023)
• Yuan Chen and Xia Li. 2023. PMAES: Prompt-mapping Contrastive Learning for Cross-prompt Automated Essay Scoring . In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 1489–1503, Toronto, Canada. Association for Computational Linguistics.

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Grading papers is hard work. “I hate it,” a teacher friend confessed to me. And that’s a major reason why middle and high school teachers don’t assign more writing to their students. Even an efficient high school English teacher who can read and evaluate an essay in 20 minutes would spend 3,000 minutes, or 50 hours, grading if she’s teaching six classes of 25 students each. There aren’t enough hours in the day. 

Could ChatGPT relieve teachers of some of the burden of grading papers? Early research is finding that the new artificial intelligence of large language models, also known as generative AI, is approaching the accuracy of a human in scoring essays and is likely to become even better soon. But we still don’t know whether offloading essay grading to ChatGPT will ultimately improve or harm student writing.

Tamara Tate, a researcher at University California, Irvine, and an associate director of her university’s Digital Learning Lab, is studying how teachers might use ChatGPT to improve writing instruction. Most recently, Tate and her seven-member research team, which includes writing expert Steve Graham at Arizona State University, compared how ChatGPT stacked up against humans in scoring 1,800 history and English essays written by middle and high school students. 

Tate said ChatGPT was “roughly speaking, probably as good as an average busy teacher” and “certainly as good as an overburdened below-average teacher.” But, she said, ChatGPT isn’t yet accurate enough to be used on a high-stakes test or on an essay that would affect a final grade in a class.

Tate presented her study on ChatGPT essay scoring at the 2024 annual meeting of the American Educational Research Association in Philadelphia in April. (The paper is under peer review for publication and is still undergoing revision.) 

Most remarkably, the researchers obtained these fairly decent essay scores from ChatGPT without training it first with sample essays. That means it is possible for any teacher to use it to grade any essay instantly with minimal expense and effort. “Teachers might have more bandwidth to assign more writing,” said Tate. “You have to be careful how you say that because you never want to take teachers out of the loop.” 

Writing instruction could ultimately suffer, Tate warned, if teachers delegate too much grading to ChatGPT. Seeing students’ incremental progress and common mistakes remain important for deciding what to teach next, she said. For example, seeing loads of run-on sentences in your students’ papers might prompt a lesson on how to break them up. But if you don’t see them, you might not think to teach it. 

In the study, Tate and her research team calculated that ChatGPT’s essay scores were in “fair” to “moderate” agreement with those of well-trained human evaluators. In one batch of 943 essays, ChatGPT was within a point of the human grader 89 percent of the time. On a six-point grading scale that researchers used in the study, ChatGPT often gave an essay a 2 when an expert human evaluator thought it was really a 1. But this level of agreement – within one point – dropped to 83 percent of the time in another batch of 344 English papers and slid even farther to 76 percent of the time in a third batch of 493 history essays.  That means there were more instances where ChatGPT gave an essay a 4, for example, when a teacher marked it a 6. And that’s why Tate says these ChatGPT grades should only be used for low-stakes purposes in a classroom, such as a preliminary grade on a first draft.

ChatGPT scored an essay within one point of a human grader 89 percent of the time in one batch of essays

Still, this level of accuracy was impressive because even teachers disagree on how to score an essay and one-point discrepancies are common. Exact agreement, which only happens half the time between human raters, was worse for AI, which matched the human score exactly only about 40 percent of the time. Humans were far more likely to give a top grade of a 6 or a bottom grade of a 1. ChatGPT tended to cluster grades more in the middle, between 2 and 5. 

Tate set up ChatGPT for a tough challenge, competing against teachers and experts with PhDs who had received three hours of training in how to properly evaluate essays. “Teachers generally receive very little training in secondary school writing and they’re not going to be this accurate,” said Tate. “This is a gold-standard human evaluator we have here.”

The raters had been paid to score these 1,800 essays as part of three earlier studies on student writing. Researchers fed these same student essays – ungraded –  into ChatGPT and asked ChatGPT to score them cold. ChatGPT hadn’t been given any graded examples to calibrate its scores. All the researchers did was copy and paste an excerpt of the same scoring guidelines that the humans used, called a grading rubric, into ChatGPT and told it to “pretend” it was a teacher and score the essays on a scale of 1 to 6. 

Older robo graders

Earlier versions of automated essay graders have had higher rates of accuracy . But they were expensive and time-consuming to create because scientists had to train the computer with hundreds of human-graded essays for each essay question. That’s economically feasible only in limited situations, such as for a standardized test, where thousands of students answer the same essay question. 

Earlier robo graders could also be gamed, once a student understood the features that the computer system was grading for. In some cases, nonsense essays received high marks if fancy vocabulary words were sprinkled in them. ChatGPT isn’t grading for particular hallmarks, but is analyzing patterns in massive datasets of language. Tate says she hasn’t yet seen ChatGPT give a high score to a nonsense essay. 

Tate expects ChatGPT’s grading accuracy to improve rapidly as new versions are released. Already, the research team has detected that the newer 4.0 version, which requires a paid subscription, is scoring more accurately than the free 3.5 version. Tate suspects that small tweaks to the grading instructions, or prompts, given to ChatGPT could improve existing versions. She is interested in testing whether ChatGPT’s scoring could become more reliable if a teacher trained it with just a few, perhaps five, sample essays that she has already graded. “Your average teacher might be willing to do that,” said Tate.

Many ed tech startups, and even well-known vendors of educational materials, are now marketing new AI essay robo graders to schools. Many of them are powered under the hood by ChatGPT or another large language model and I learned from this study that accuracy rates can be reported in ways that can make the new AI graders seem more accurate than they are. Tate’s team calculated that, on a population level, there was no difference between human and AI scores. ChatGPT can already reliably tell you the average essay score in a school or, say, in the state of California. 

Questions for AI vendors

At this point, it is not as accurate in scoring an individual student. And a teacher wants to know exactly how each student is doing. Tate advises teachers and school leaders who are considering using an AI essay grader to ask specific questions about accuracy rates on the student level:   What is the rate of exact agreement between the AI grader and a human rater on each essay? How often are they within one-point of each other?

The next step in Tate’s research is to study whether student writing improves after having an essay graded by ChatGPT. She’d like teachers to try using ChatGPT to score a first draft and then see if it encourages revisions, which are critical for improving writing. Tate thinks teachers could make it “almost like a game: how do I get my score up?” 

Of course, it’s unclear if grades alone, without concrete feedback or suggestions for improvement, will motivate students to make revisions. Students may be discouraged by a low score from ChatGPT and give up. Many students might ignore a machine grade and only want to deal with a human they know. Still, Tate says some students are too scared to show their writing to a teacher until it’s in decent shape, and seeing their score improve on ChatGPT might be just the kind of positive feedback they need. 

“We know that a lot of students aren’t doing any revision,” said Tate. “If we can get them to look at their paper again, that is already a win.”

That does give me hope, but I’m also worried that kids will just ask ChatGPT to write the whole essay for them in the first place.

This story about  AI essay scoring was written by Jill Barshay and produced by  The Hechinger Report , a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for  Proof Points   and other  Hechinger newsletters .

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Automated essay scoring to assess digital literacy competence

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Yeni Anistyasari , Ekohariadi , Tri Rijanto , Shintami C. Hidayati; Automated essay scoring to assess digital literacy competence. AIP Conf. Proc. 24 May 2024; 3116 (1): 060030. https://doi.org/10.1063/5.0210437

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Students nowadays must acquire critical, cognitive, social, operational, emotional, and projective digital literacy skills, particularly in the Faculty of Engineering. An essay test is one technique that may be used to measure them. However, evaluating essay tests is time-consuming and subjective, resulting in variable measuring outcomes. (1) analyzing the construct validity of digital literacy competencies, (2) analyzing the reliability of the score obtained from the automated essay scoring test, and (3) analyzing the difference between scores obtained from automated essay scoring and scores assessed manually were the objectives of this study. The measurement results data were studied using factor analysis to evaluate construct validity, Cronbach alpha estimation to generate reliability coefficients, and inter-rater kappa to ascertain the level of concordance between scores acquired through automated essay scoring and scores assessed manually. The analysis results indicate that the digital literacy competence construct is legitimate, that the scores received from automated essay scoring tests are deemed credible, and that the scores acquired from automated essay scoring and those assessed manually are equivalent.

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