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SmartMarq: Human and AI Essay marking

Easily manage the essay marking process, then use modern machine learning models to serve as a second or third rater, reducing costs and timelines.

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Create your scoring rubrics and performance descriptors

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Assign essays to be scored, then view results

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Raters can easily move through and leave scores and comments

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Implement automated essay scoring to flag unusual scores

SmartMarq will streamline your essay marking process

SmartMarq makes it easy to implement large-scale, professional essay scoring.

• Reduce timelines for marking
• Increase convenience by managing fully online
• Implement business rules to ensure quality
• Once raters are done, run the results through our AI to train a custom machine learning model for your data, obtaining a second “rater.”

Note that our powerful AI scoring is customized, specific to each one of your prompts and rubrics – not developed with a shotgun approach based on general heuristics.

Fully integrated into our FastTest ecosystem

We pride ourselves on providing a single ecosystem with configurable modules that covers the entire test development and delivery cycle.  SmartMarq is available both standalone, and as part of our online delivery platform. If you have open-response items, especially extended constructed response (ECR) items, our platforms will improve the process needed to mark these items.  Leverage our user-friendly, highly scalable online marking module to manage hundreds (or just a few) raters, with single or multi-marking situations.

“FastTest reduced the workhours needed to mark our student essays by approximately 60%, cutting it from a multi-day district-wide project to a single day!”

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Students write their essays or other ECR responses

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Users (e.g., teachers) log in and mark student responses on your specified rubrics, as well as flag responses or leave comments. Admins can adjudicate any disagreements.

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Examinees will be automatically scored.  For example, if your test has 40 multiple choice items and an essay with two 5-point rubrics, the total score is 50.  We also support the generalized partial credit model from item response theory, or exporting results to analyze in other software like FACETS.

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Simply upload your student essays and human marking results, and our AI essay scoring system will provide an additional set of marks.

Need a complete platform to manage the entire assessment cycle, from item banking to online delivery to scoring?  FastTest provides the ideal solution.  It includes an integrated version of SmartMarq with advanced options like scoring rubrics with the Generalized Partial Credit Model .

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e-rater ®  Scoring Engine

Evaluates students’ writing proficiency with automatic scoring and feedback

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ETS is a global leader in educational assessment, measurement and learning science. Our AI technology, such as the e-rater ® scoring engine, informs decisions and creates opportunities for learners around the world.

The e-rater engine automatically:

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This ETS capability identifies features related to writing proficiency.

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Join the community, add a new evaluation result row, automated essay scoring.

24 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.

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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

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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]

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Neural Automated Essay Scoring Incorporating Handcrafted Features

Masaki Uto , Yikuan Xie , Maomi Ueno

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Improving Automated Essay Scoring by Prompt Prediction and Matching

1 School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China

Tianbao Song

2 School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China

Weiming Peng

Associated data.

Publicly available datasets were used in this study. These data can be found here: http://hsk.blcu.edu.cn/ (accessed on 6 March 2022).

Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational application in the field of natural language processing. Recently, Pre-training techniques have been used to improve performance on downstream tasks, and many studies have attempted to use pre-training and then fine-tuning mechanisms in an essay scoring system. However, obtaining better features such as prompts by the pre-trained encoder is critical but not fully studied. In this paper, we create a prompt feature fusion method that is better suited for fine-tuning. Besides, we use multi-task learning by designing two auxiliary tasks, prompt prediction and prompt matching, to obtain better features. The experimental results show that both auxiliary tasks can improve model performance, and the combination of the two auxiliary tasks with the NEZHA pre-trained encoder produces the best results, with Quadratic Weighted Kappa improving 2.5% and Pearson’s Correlation Coefficient improving 2% on average across all results on the HSK dataset.

1. Introduction

Automated essay scoring (AES), which aims to automatically evaluate and score essays, is one typical application of natural language processing (NLP) technique in the field of education [ 1 ]. In earlier studies, a combination of handcrafted design features and statistical machine learning is used [ 2 , 3 ], and with the development of deep learning, neural network-based approaches gradually become mainstream [ 4 , 5 , 6 , 7 , 8 ]. Recently, pre-trained language models have gradually become the foundation module of NLP, and the paradigm of pre-training, then fine-tuning, is also widely adopted. Pre-training is the most common method for transfer learning, in which a model is trained on a surrogate task and then adapted to the desired downstream task by fine-tuning [ 9 ]. Some research has attempted to use pre-training modules in AES tasks [ 10 , 11 , 12 ]. Howard et al. [ 10 ] utilize the pre-trained encoder as a feature extraction module to obtain a representation of the input text and update the pre-trained model parameters based on the downstream text classification task by adding a linear layer. Rodriguez et al. [ 11 ] employ a pre-trained encoder as the essay representation extraction module for the AES task, with inputs at various granularities of the sentence, paragraph, overall, etc., and then use regression as the training target for the downstream task to further optimize the representation. In this paper, we fine-tune the pre-trained encoder as a feature extraction module and convert the essay scoring task into regression as in previous studies [ 4 , 5 , 6 , 7 ].

The existing neural methods obtain a generic representation of the text through a hierarchical model using convolutional neural networks (CNN) for word-level representation and long short-term memory (LSTM) for sentence-level representation [ 4 ], which is not specific to different features. To enhance the representation of the essay, some studies have attempted to incorporate features such as prompt [ 3 , 13 ], organization [ 14 ], coherence [ 2 ], and discourse structure [ 15 , 16 , 17 ] into the neural model. These features are critical for the AES task because they help the model understand the essay while also making the essay scoring more interpretable. In actual scenarios, prompt adherence is an important feature in essay scoring tasks [ 3 ]. The hierarchical model is insensitive to changes in the corresponding prompt for the essay and always assigns the same score for the same essay, regardless of the essay prompt. Persing and Ng [ 3 ] propose a feature-rich approach that integrates the prompt adherence dimension. Ref. [ 18 ] improves document modeling with a topic word. Li et al. [ 7 ] utilizes a hierarchical structure with an attention mechanism to construct prompt information. However, the above feature fusion methods are unsuitable for fine-tuning.

The two challenges in effectively incorporating pre-trained models into AES feature representation are the data dimension and the methodological dimension. For the data dimension, the use of fine-tuning approaches to transfer the pre-trained encoder to downstream tasks frequently necessitates sufficient data, and there has been more research on both training and testing data from the same target prompt [ 4 , 5 ], but the data size is relatively small, varying between a few hundred and a few thousand, and pre-trained encoders cannot be fine-tuned well. In order to solve this challenge, we use the whole training set, which includes various prompts. In terms of methodology, we employ the pre-training and multi-task learning (MTL) paradigms, which can learn features that cannot be learned in a single task through joint learning, learning to learn, and learning with auxiliary tasks [ 19 ], etc. MTL methods have been applied to several NLP tasks, such as text classification [ 20 , 21 ], semantic analysis [ 22 ] et al. Our method creates two auxiliary tasks that need to be learned alongside the main task. The main task and auxiliary tasks can increase each other’s performance by sharing information and complementing each other.

In this paper, we propose an essay scoring model based on fine-tuning that utilizes multi-task learning to fuse prompt features by designing two auxiliary tasks, prompt prediction, and prompt matching, which is more suitable for fine-tuning. Our approach can effectively incorporate the prompt feature in essays and improve the representation and understanding of the essay. The paper is organized as follows. In Section 2 , we first review related studies. We describe our method and experiment in Section 3 and Section 4 . Section 5 presents the findings and discussions. Finally, in Section 6 , we provide a conclusion, future work, and the limitations of the paper.

2. Related Work

Pre-trained language models, such as BERT [ 23 ], BERT-WWM [ 24 ], RoBERTa [ 25 ], and NEZHA [ 26 ], have gradually become a fundamental technique for NLP, with great success on both English and Chinese tasks [ 27 ]. In our approach, we use the BERT and NEZHA feature extraction layers. BERT is the abbreviation of Bidirectional Encoder Representations from Transformers, and it is based on transformer blocks that are built using the attention mechanism [ 28 ] to extract semantic information. It is trained on two unsupervised tasks using large-scale datasets: masked language model (MLM) and next sentence prediction (NSP). NEZHA is a Chinese pre-training model that employs functional relative positional encoding and whole word masking (WWM) rather than BERT. The pre-training then the fine-tuning mechanism is widely used in downstream NLP tasks, including AES [ 11 , 12 , 15 ]. Mim et al. [ 15 ] propose a pre-training approach for evaluating the organization and argument strength of essays based on modeling coherence. Song et al. [ 12 ] present a multi-stage pre-training method for automated Chinese essay scoring that consists of three components: weakly supervised pre-training, supervised cross-prompt fine-tuning, and supervised target-prompt fine-tuning. Rodriguez et al. [ 11 ] use BERT and XLNET [ 29 ] for representation and fine-tuning of English corpus.

The essay prompt introduces the topic, offers concepts, and restricts both content and perspective. Some studies have attempted to enhance the AES system by incorporating prompt features in many ways, such as by integrating prompt information to determine if an essay is off-topic [ 13 , 18 ] or by considering prompt adherence as a crucial indicator [ 3 ]. Louis and Higgins [ 13 ] improve model performance by expanding prompt information with a list of related words and reducing spelling errors. Persing and Ng [ 3 ] propose a feature-rich method for incorporating the prompt adherence dimension via manual annotation. Klebanov et al. [ 18 ] also improve essay modeling with topic words to quantify the overall relevance of the essay to the prompt, and the relationship between prompt adherence scores and total essay quality is also discussed. The methods described above mostly employ statistical machine learning, prompt information is enriched by annotation and the construction of datasets, as well as the construction of word lists and topic word mining. While all of them are making great progress, the approaches they are employing are more difficult to directly transfer to fine-tuning. Li et al. [ 7 ] propose a shared model and an enhanced model (EModel), and utilize a neural network hierarchical structure with an attention mechanism to construct features of the essay such as discourse, coherence, relevancy, and prompt. For the representation, the paper employs GloVe [ 30 ] rather than a pre-trained model. In the experiment section, we compared our method to the sub-module of EModel (Pro.) which incorporates the prompt feature.

3.1. Motivation

Although previous studies on automated essay scoring models for specific prompts have shown promising results, most research focuses on generic features of essays. Only a few studies have focused on prompt feature extraction, and no one has attempted to use a multi-task approach to make the model capture prompt features and be sensitive to prompts automatically. Our approach is motivated by capturing prompt features to make the model aware of the prompt and using pre-training and then the fine-tuning mechanism for AES. Based on this motivation, we use a multi-task learning approach to obtain features that are more applicable to Essay Scoring (ES) by adding essay prompts to the model input and proposing two auxiliary tasks: Prompt Prediction ( PP ) and Prompt Matching ( PM ). The overall architecture of our model is illustrated in Figure 1 .

The proposed framework. “一封求职信” is the prompt of the essay, the English translation is “A cover letter”. “主管您好” means “Hello Manager”. The prompt and essay are separated by [SEP].

3.2. Input and Feature Extraction Layer

The input representation for a given essay is built by adding the corresponding token embeddings E t o k e n , segment embeddings E s e g m e n t , and position embeddings E p o s i t i o n . To fully exploit the prompt information, we concatenate the prompt in front of the essay. The first token of each input is a special classification token [CLS], and the prompt and essay are separated by [SEP]. The token embedding of the j -th essay in the i -th prompt can be expressed as Equation ( 1 ), E s e g m e n t and E p o s i t i o n are obtained from the tokenizer of the pre-train encoder.

We utilize the BERT and NEZHA as feature extraction layers. The final hidden state corresponding to the [CLS] token is the essay representation r e for essay scoring and subtasks.

3.3. Essay Scoring Layer

We view essay scoring as a regression task. To enable data mapping regression problems, the real scores are scaled to the range [ 0 , 1 ] for training and rescaled during evaluation, according to the existing studies:

where s i j is the scaled score for i -th prompt j -th essay, and s c o r e i j is the actual score for i -th prompt j -th essay, m a x s c o r e i and m i n s c o r e i are the maximum and minimum of the real scores for the i -th prompt. The input is essay representation r e from the pre-trained encoder, which is fed into a linear layer with a sigmoid activation function:

where s ^ is the predicted score by AES system, σ is the sigmoid function, W e s is a trainable weights, and b e s is a bias. The essay scoring (es) training objective is described as:

3.4. Subtask 1: Prompt Prediction

The definition of prompt prediction is giving an essay to determine which prompt it belongs to. We view prompt prediction as a classification task. The input is essay representation r e , which is fed into a linear layer with a softmax function. The formula is given by Equation ( 5 ):

where u ^ is the probability distribution of classification results, W p p is a parameter matrix, and b p p is a bias. The loss function is formalized as follows:

where u k is the real prompt label for the k -th sample, p p p k c is the probability that the k -th sample belongs to the c -th category, C denotes the number of prompts, which in this study is ten.

3.5. Subtask 2: Prompt Matching

The definition of prompt matching is giving a pair of a prompt and an essay, and to decide if the essay and the prompt are compatible. We consider prompt matching to be a classification task. The following is the formula:

where v ^ is the probability distribution of matching results, W p m is a parameter matrix, and b p m is a bias. The objective function is shown in Equation ( 9 )

where v k indicates whether the input prompt and essay match. p p m k m is the likelihood that the matching degree of k -th sample falls into category m. m denotes the matching degree, 0 for a match, 1 for a dismatch. The distinction between prompt prediction and prompt matching is that as the number of prompts increases, the difference in classification targets leads to increasingly obvious differences in task difficulty, sample distribution and diversity, and scalability.

3.6. Multi-Task Loss Function

The final loss function for each input is a weighted sum of the loss functions for essay scoring and two subtasks: prompt prediction and prompt matching, with the loss formalized as follows:

where α , β , and γ are non-negative weights assigned in advance to balance the importance of the three tasks. Because the objective of this research is to improve the AES system, the main task should be given more weight than the two auxiliary tasks. The optimal parameters in this paper are α : β = α : γ = 100:1, and in Section 5.3 , we design experiments to figure out the optimal value interval for α , β , and γ .

4. Experiment

4.1. dataset.

We use HSK (HSK is the acronym of Hanyu Shuiping Kaoshi, which is Chinese Pinyin for the Chinese Proficiency Test). Dynamic Composition Corpus ( http://hsk.blcu.edu.cn/ (accessed on 6 March 2022)) as our dataset as in existing studies [ 31 ]. HSK is also called “TOEFL in Chinese”, which is a national standardized test designed to test the proficiency of non-native speakers of Chinese. The HSK corpus includes 11,569 essays composed by foreigners from more than thirty different nations or regions in response to more than fifty distinct prompts. We eliminate any prompts with fewer than 500 student writings from the HSK dataset to constitute the experimental data. The statistical results of the final filtered dataset are provided in Table 1 , which comprises 8878 essays across 10 prompts taken from the actual HSK test. Each essay score ranges from 40 to 95 points. We divide the entire dataset at random into the training set, validation set, and test set in the ratio of 6:2:2. To alleviate the problem of insufficient data under a single prompt, we apply the entire training set that consists of different prompts for fine-tuning. We test every prompt individually as well as the entire test set during the testing phase and utilize the same 5-fold cross-validation procedure as [ 4 , 5 ]. Finally, we report the average performance.

HSK dataset statistic.

4.2. Evaluation Metrics

For the main task, we use the Quadratic Weighted Kappa (QWK)approach, which is widely used in AES [ 32 ], to analyze the agreement between prediction scores and the ground truth. QWK can be calculated by Equations ( 11 ) and ( 12 )

where i and j are the golden score of the human rater and the AES system score, and each essay has N possible ratings. Second, calculate the QWK score using Equation ( 12 ).

where O i , j denotes the number of essays that receive a rating i by the human rater and a rating j by the AES system. The expected rating matrix Z is histogram vectors of the golden rating and AES system rating and normalized so that the sum of its elements equals the sum of its elements in O . We also utilize Pearson’s Correlation Coefficient (PCC) to measure the association as in previous studies [ 3 , 32 , 33 ], which quantifies the degree of linear dependency between two variables and describes the level of covariation. In contrast to the QWK metric, which evaluates the agreement between the model output and the gold standard, we use PCC to assess whether the AES system ranks essays similarly to the gold standard, indicating the capacity of the AES system to appropriately rank texts, i.e., high scores ahead of low scores. For auxiliary tasks, we consider prompt prediction and prompt matching as classification problems and use macro-F1 score (F1), and accuracy (Acc.) as evaluation metrics.

4.3. Comparisons

Our model is compared to the baseline models listed below. The former three are existing neural AES methods, and we experiment with both character and word input when training for comparison. The fourth method is to fine-tune the pre-trained model, and the rest are variations of our proposed method.

CNN-LSTM [ 4 ]: This method builds a document using CNN for word-level representation and LSTM for sentence-level representation, as well as the addition of a pooling layer to obtain the text representation. Finally, the score is obtained by applying the linear layer of the sigmoid function.

CNN-LSTM-att [ 5 ]: This method incorporates an attention mechanism into both the word-level and sentence-level representations of CNN-LSTM.

EModel (Pro.): This method concatenates the prompt information in the input layer of CNN-LSTM-att, which is a sub-module of [ 7 ].

BERT/NEZHA-FT: This method is used to fine-tune the pre-trained model. To obtain the essay representation, we directly feed an essay into the pre-trained encoder as the input. We choose the [CLS] embedding as essay representations and feed them into a linear layer of the sigmoid function for scoring.

BERT/NEZHA-concat: The difference between this method and fine-tune is that the input representation concatenates the prompt to the front of the essay in token embedding, as in Figure 1 .

BERT/NEZHA-PP: This model incorporates prompt prediction as an auxiliary task, with the same input as the concat model and the output using [CLS] as the essay representation. A linear layer with the sigmoid function is used for essay scoring, and a linear layer with the softmax function is used for prompt prediction.

BERT/NEZHA-PM: This model includes prompt matching as an auxiliary task. In the input stage of constructing the training data, there is a 50% probability that the prompt and the essay are mismatched. [CLS] embedding is used to represent the essay. A linear layer with the sigmoid function is used for essay scoring, and a linear layer with the softmax function is used for prompt matching.

BERT/NEZHA-PP&PM: This model utilizes two auxiliary tasks, prompt prediction, and prompt matching, with the same inputs and outputs as the PM model. The output layer of the auxiliary tasks is the same as above.

4.4. Parameter Settings

We use BERT ( https://github.com/google-research/bert (accessed on 11 March 2022)) and NEZHA ( https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-TensorFlow (accessed on 11 March 2022)) as pre-trained encoder. To obtain tokens and token embeddings, we employ the tokenizer and vocabulary of the pre-trained encoder. The parameters of the pre-trained encoder are learnable during both the fine-tuning and training phases. The maximum length of the input is set to 512 and Table 2 includes additional parameters. The baseline models, CNN-LSTM and CNN-LSTM-att, are trained from scratch, and their parameters are shown in Table 2 . Our experiments are carried out on NVIDIA TESLA V100 32 G GPUs.

Parameter settings.

5. Results and Discussions

5.1. main results and analysis.

We report our experimental results in Table 3 and Table A1 (Due to space limitations, this table is included in Appendix A ). Table A1 illustrates the average QWK and PCC for each prompt. Table 3 shows QWK and PCC across the entire test set and the average results of each prompt test set. As shown in Table 3 , we can find that the proposed auxiliary tasks (PP, PM, and PP&PM) (line 8–10 & 13–15) outperform other contrast models on both QWK and PCC, PP&PM models with the pre-trained encoder, BERT, and NEZHA, outperform PP and PM on QWK. In terms of the PCC metric, PM models exceeded the other two models except for the average result with the NEZHA encoder. The findings above indicate that our proposed two auxiliary tasks are both effective.

QWK and PCC for the total test set and Average QWK and PCC for each prompt test set; † denotes input as a character; ‡ denotes input as word. The best results are in bold.

On Total test set, our best results, a pre-trained encoder with PM and PP, are higher compared to fine-tuning method and EModel(Pro.), exceed the strong baseline concat model by 1.8% with BERT and 2.3% with NEZHA on QWK, and get a generally consistent correlation. It is shown from Table 3 that our proposed models also yield similar results to the Average test set, 1.6% of BERT and 2% of NEZHA on QWK of PP&PM models compared to concat model, 2% of BERT and 2.5% of NEZHA on QWK of PP&PM models compared to fine-tuning model, and competitive results on PCC metric. Using the multi-task learning approach and fine-tuning comparison, our proposed approach outperforms the baseline system on both QWK and PCC, indicating that better essay representation can be obtained through multi-tasking learning. Furthermore, when compared to the concat model with fused prompt representation, our proposed approach outperform the baseline in QWK scores, but line 10 and line 15 in Table 3   Total track PCC values are lower within 1% of the baseline. It demonstrates that our proposed auxiliary task is effective in representing the essay prompt.

We train the hierarchical model (line 1–4) using character and word as input, respectively, and the results show that using the character for training is generally better, with the best results in Total and Average being more than 4% lower than those with the pre-training method. The results indicate that using pre-trained encoders both BERT and NEZHA for feature extraction works well on the HSK dataset. The pre-training model comparison reveals that BERT and NEZHA are competitive, with NEZHA delivering the best results.

Results of each prompt with BERT and NEZHA are displayed in Figure 2 . The results of our proposed models (PP, PM, and PP&PM) have made positive progress on several prompts. Among them, the results of PP&PM, in addition, to prompt 1 and prompt 5, extend beyond the two baselines of fine-tuning and concat . The results indicate that our proposed auxiliary tasks to incorporate prompt is generic and can be employed with a range of genres and prompts. The primary cause of the results of individual prompts being suboptimal is that the hyperparameters of loss function α , β , and γ are not adjusted specifically for each prompt and we will further analyze the reasons for this in Section 5.3 .

( a ) Results of each prompt with BERT pre-trained encoder on QWK; ( b ) Results of each prompt with NEZHA pre-trained encoder on QWK.

5.2. Result and Effect of Auxiliary Tasks

Table 4 depicts the results of the auxiliary tasks (PP and PM) on validation set, the accuracy and F1 are both greater than 85% for BERT and 90% for NEZHA, and the model is well trained in the auxiliary task, when compared to both pre-trained models BERT and NEZHA, the latter produces better. The results of auxiliary tasks with NEZHA perform better as feature extraction modules.

Accuracy and F1 for PP and PM on validation set.

Comparing the contribution of PP and PM, as shown in Table A1 and Table 3 and Figure 3 , the contribution of PM is higher and more effective. Figure 3 a,b illustrate radar graphs of various pre-trained encoders of PP and PM across 10 prompts utilizing QWK metrics. Figure 3 a shows that the QWK value of PM is higher than PP in all but prompt 9 with BERT encoder, and Figure 3 b demonstrates that the results of PM are 60% better compared to those of PP, implying that PM is also superior to PP for a specific prompt. The PM and PP comparison results for the Total and Average datasets are provided in Figure 3 c,d. Except for the PM model with the NEZHA pre-trained encoder, which has a slightly lower QWK than the PP model, all models that use PM as a single auxiliary task perform better, further demonstrating the superiority of prompt matching in prompt representing and incorporating.

( a ) Radar graph of BERT-PP&BERT-PM; ( b ) Radar graph of NEZHA-PP&NEZHA-PM; ( c ) Results of PP and PM on QWK; ( d ) Results of PP and PM on PCC.

5.3. Effect of Loss Weight

We examine how the ratio of loss weight parameters β and γ affects the model. Figure 4 a shows that the model works best when the ratio is 1:1 on both QWK and PCC metrics. Figure A1 depicts the QWK results for various β and γ ratios, as well as revealing that the model produces the greatest results at around 1:1 for different prompts, except for prompts 1, 5, and 6, and the same is true for the average results. Concerning the issue of our model being suboptimal for individual prompts, Figure A1 illustrates that the best results for prompts 1, 5, and 6 are not achieved at 1:1, suggesting that it is inappropriate for such parameters in these prompts. Because we disorder the entire training set and fix the β and γ ratio before testing it independently, the parameters of the different prompts cannot be dynamically adjusted within a single training procedure. The reasons are to address the lack of data and also to focus more on the average performance of the model, which also prevents the model from overfitting for specific prompts. Compared to the results in Table A1 , NEZHA-PP and NEZHA-PM both outperform the baselines and the PP&PM model for prompt 1, indicating that both PP and PM can enhance the results when employed separately. For prompt 5, NEZHA-PP performs better than NEZHA-PM, showing that PP plays a greater role. The PP&PM model is already the best result for prompt 6, even though the 1:1 parameter is not optimal in Figure A1 , demonstrating that there is still potential for improvement. The information above reveals that different prompts have varying degrees of difficulty for joint training and parameter optimization of the main and auxiliary tasks, along with different conditions of applicability for the two auxiliary tasks we presented.

( a ) The effect of PP&PM in different β / γ ratios of QWK and PCC on Total dataset, we fix the value of α in this section of the experiment.; ( b ) The smoothing results for training losses across all tasks; ( c ) The results of different α : β (PP), α : γ (PM), and α : β : γ (PP&PM) ratios on QWK.

We also measure the effect of α on the model, where we fix the β / γ ratio constant at 1:1. Figure 4 c demonstrates that the PP, PM, and PP&PM models are all optimal at α : β = α : γ = 100:1, with the best QWK values for PP&PM, indicating that our suggested method of combining two auxiliary tasks for joint training is effective. The observation of [ 1 , 100 ] shows that when the ratio is small, the main task cannot be trained well, the two auxiliary tasks have a negative impact on the main task, but the single auxiliary task has less impact, indicating that multiple auxiliary tasks are more difficult to train concurrently than a single auxiliary task. In addition, future research should consider how to dynamically optimize the parameters of multiple tasks.

The training losses for ES, PP, and PM are included in Figure 4 b, and it can be seen that the loss of the main task decreases rapidly in the early stage, and the model converges around 6000 steps. The reason for faster model convergence in PM is that the task is a dichotomous classification compared to PP, which is a ten classification, and additionally, among the ten prompts, prompt 6 “A letter to parent” and prompt 9 “Parents are children’s first teachers” are more similar, making PP more difficult. As a result, further research into how to select the appropriate weight ratio and design more matching auxiliary tasks is required.

6. Conclusions and Future Work

This paper presents a pre-training and then fine-tuning model for automated essay scoring. The model incorporates the essay prompts to the model input and obtains better features more applicable to essay scoring by multi-task learning with two auxiliary tasks, prompt prediction, and prompt matching. Experiments demonstrate that the model outperforms baselines in results measured by the QWK and PCC on average across all results on the HSK dataset, indicating that our model is substantially better in terms of agreement and association. The experimental results also show that both auxiliary tasks can effectively improve the model performance, and the combination of the two auxiliary tasks with the NEZHA pre-trained encoder yields the best results, with QWK enhancing 2.5% and PCC improving 2% compared to the strong baseline, the concatenate model, on average across all results on the HSK dataset. When compared to existing neural essay scoring methods, the experimental results show that QWK improves by 7.2% and PCC improves by 8% on average across all results.

Although our work has enhanced the effectiveness of the AES system, there are still limitations. Regarding the data dimension, this research primarily investigates fusing prompt features in Chinese; other languages are not examined extensively. Nevertheless, our method is more convenient for migration than the manual annotation approach, and other languages can be directly migrated. Furthermore, other features in different languages can use our method to create similar auxiliary tasks for information fusion. Moreover, as the number of prompts grows, the difficulty of training for prompt prediction increases, and we will consider combining prompts with genre and other information to design auxiliary tasks suitable for more prompts, as well as attempting to find a balance between the number of essays and the number of prompts to make prompt prediction more efficient. The parameters of the loss function are now defined empirically at the methodological level, which is not conducive to additional auxiliary activities. In future work, we will optimize the parameter selection scheme and build dynamic parameter optimization techniques to accommodate variable numbers of auxiliary tasks. In terms of application, our approaches focus on fusing textual information in prompts, while they do not cover all prompt forms. Our system now requires additional modules for the chart and picture prompt. In future research, we will experiment with multimodal prompt data to improve the application scenarios of the AES system.

Abbreviations

The following abbreviations are used in this manuscript:

QWK and PCC for each prompt on HSK dataset, † denotes input as character; ‡ denotes input as word. The best results are in bold.

The effect of PP&PM in different β / γ ratios of QWK across all dataset, we fix the value of α in this section of the experiment.

Funding Statement

This research was funded by the National Natural Science Foundation of China (Grant No.62007004), the Major Program of the National Social Science Foundation of China (Grant No.18ZDA295), and the Doctoral Interdisciplinary Foundation Project of Beijing Normal University (Grant No.BNUXKJC2020).

Author Contributions

Conceptualization and methodology, J.S. (Jingbo Sun); writing—original draft preparation, J.S. (Jingbo Sun) and T.S.; writing—review and editing, T.S., J.S. (Jihua Song) and W.P. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

College of Ed contributes to Purdue AI & data science via initiative, miniseries

The P urdue University College of Education is developing an initiative to cover all of the artificial intelligence (AI) and data science work already being done by its faculty. This spring the College launched a new webpage, AI and Data Science in Education , to showcase these efforts and provide resources.

Phillip J. VanFossen , interim dean of the College, convened an AI Working Group in 2023 to consider how the College would contribute to Purdue’s strategic initiative surrounding AI and data science, Purdue Computes . The Education AI Working Group includes 16 Education faculty and postdoctoral researchers  to guide and coordinate its current and future efforts in this quickly evolving area.

“We are leveraging our comparative advantage in P-12 education to provide professional development and training to our school-based colleagues,” said VanFossen. “We offer courses, graduate certificates, and degree programs that provide training for students in the analyses of large sets of numerical, textual, and multimodal data and practical applications of AI tools.”

“Our working group leverages experts with established records in AI and data science for personalized learning, assessment, and curriculum design,” said William R. Watson , director of the Purdue Center for Serious Games and Learning in Virtual Environments , professor of learning design and technology, and leader of the College’s AI initiative. “Our College is already firmly integrated in campus-wide AI initiatives and offering coursework and programs of our own.”

The College’s initiatives include several large research grants in projects ranging from generative-AI in physics to value-based reasoning. Last year the college hired two Ross-Lynn Research Scholar postdoctoral researchers, Amogh Sirnoorkar and Yuanfang Liu . Both have expertise in AI to further assist with AI research, grants, professional development, and program development

“AI is not a new phenomenon, but the ease of access to everyday users has been sudden and transformative,” Watson said. “Having such powerful toolsets on our phones and embedded in our office software highlights how society at large and education specifically must change to accommodate the new possibilities, opportunities, and challenges the ascent of AI brings.”

Part of the initiatives are education courses relating to AI. Sirnoorkar developed a course for the initiative, “Artificial Intelligence in STEM Education” (EDCI 59100/PHYS 59000). It included eight invited speakers from collaborating schools and departments at Purdue and from the University of Texas at Austin, and students created four projects during the course. Projects included:

• AI Literacy Competencies for K-12 Teachers: A Systematic Literature Review
• Analyzing contemporary policies surrounding the use of AI in U.S. higher education institutions
• Enhancing STEM Education Through AI-XR Integration: A Systematic Literature Review of Trends, Applications, and Challenges Content Analysis of Pedagogical Relationships Involving Generative-AI Supported Teaching and Learning Practices in Physics Education
• Content Analysis of Pedagogical Relationships Involving Generative-AI Supported Teaching and Learning Practices in Physics Education

Additionally, the AI in STEM Education course will help support the College’s Gifted Education Research & Resource Institute Residential Summer Camp via a secondary-level course called “How to Train Your AI Learning Coach: A Personalized Learning Adventure.”

The College’s new AI webpage also includes a list of the College’s faculty publications, and video resources such as a new five-part miniseries entitled “Craft Data Science Insights.” Developed by Hua-Hua Chang , the Charles R. Hicks Chair Professor of educational psychology and research methodology, this miniseries brings together five brilliant young minds from education, psychology, learning science, and survey methodology to share their innovative solutions to challenges powered by AI and data science.

The inaugural session of the Craft Data Science Insights miniseries took place on February 26 with Georgetown University’s Dr. Qiwei He speaking on “ Sequence Mining on Process Data in Digital-Based Large-Scale Assessments .” Fifty-seven participants joined to watch the virtual presentation. Watch Dr. He’s video .

A second presentation took place on March 20 with Dr. Chanjin Zheng of East China Normal University presenting on “ Psychometrics Empowering Large Language Models in Chinese Essay Automated Scoring .” Watch Dr. Zheng’s video .

The third lecture took place on March 26 as Dr. Susu Zhang of the University of Illinois at Urbana-Champaign discussed “ Exploring the NAEP Math Achievement Gap: Insights from Test-Taking Process Data . ” Watch Dr. Zhang’s video .

The fourth lecture happened on March 28  when University of Notre Dame’s Dr. Ying Cheng presented “ How Early, Accurate, and Fair Can We Predict Student Learning in Foundational STEM Courses? ” Watch Dr. Cheng’s video .

The College’s fifth and final Craft Data Science Insights lecture will take place on April 16, with the University of Michigan’s Dr. Tuba Suzer-Gurtekin, University of Michigan presenting “ Introduction to Mixed-Mode Surveys ”.

“We are confident that this miniseries will offer valuable insights, enriching our collective understanding of these dynamic fields,” Chang said. “Specifically, it will shed light on how researchers from non-science and technology backgrounds can make a meaningful impact in the era of AI and Big Data.”

The working group is also planning an April 19 “AI-Ed Fusion: Symposium on STEM Education in the Era of AI” . The all-day symposium will offer a keynote address by Dr. Kristen DiCerbo , the chief learning officer of Khan Academy . It will also include research and application presentations by faculty and graduate students, offering a sampling of some of the current projects and activities taking place within the College. Register for this free event https://tinyurl.com/yc3sknyd

According to Watson, the working group is also planning a conference on AI for K-12 stakeholders for the fall of 2024.

“The College is home to faculty scholars, centers and funded grant projects engaged in interdisciplinary research to leverage AI tools and to collect and analyze data that seeks to transform learning and teaching, inform policy and make a difference in the lives of culturally and linguistically diverse students, families and communities,” said Wayne E. Wright , professor and associate dean for research, graduate programs and faculty development.

Sources: Phillip J. VanFossen; Wayne E. Wright, [email protected] ; and William R. Watson [email protected]

Webpage: Artificial Intelligence (AI) and Data Science in Education

Automated Pipeline for Multi-lingual Automated Essay Scoring with ReaderBench

• Published: 01 April 2024

Cite this article

• Stefan Ruseti   ORCID: orcid.org/0000-0002-0380-6814 1 ,
• Ionut Paraschiv 1 ,
• Mihai Dascalu   ORCID: orcid.org/0000-0002-4815-9227 1 , 2 &
• Danielle S. McNamara 3  

Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline integrated into the ReaderBench platform designed to simplify the process of training AES models by automating both feature extraction and architecture tuning for any multilingual dataset uploaded by the user. The dataset must contain a list of texts, each with potentially multiple annotations, either scores or labels. The platform includes traditional ML models relying on linguistic features and a hybrid approach combining Transformer-based architectures with the previous features. Our method was evaluated on three publicly available datasets in three different languages (English, Portuguese, and French) and compared with the best currently published results on these datasets. Our automated approach achieved comparable results to state-of-the-art models on two datasets, while it obtained the best performance on the third corpus in Portuguese.

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Data Availability

All three datasets used for evaluation are publicly available: ASAP - https://www.kaggle.com/c/asap-aes ; Essay-BR - https://github.com/lplnufpi/essay-br ; French FakeNews https://huggingface.co/datasets/readerbench/fakenews-climate-fr l .

Code Availability

The code repository is publicly available on GitHub https://github.com/readerbench/ReaderBenchAPI . The platform can be accessed at https://readerbench.com .

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Acknowledgements

We would like to thank Emanuel Tertes and Pavel Betiu who supported us in developing the new interface for ReaderBench.

This work was supported by a grant from the Ministry of Research, Innovation and Digitalization, project CloudPrecis “Increasing UPB’s research capacity in Cloud technologies and massive data processing”, Contract Number 344/390020/06.09.2021, MySMIS code: 124812, within POC. The research reported here was also supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A180261 to Arizona State University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

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Stefan Ruseti, Ionut Paraschiv & Mihai Dascalu

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Department of Psychology, Arizona State University, Tempe, AZ, USA

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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Stefan Ruseti. The first draft of the manuscript was written by Ionut Paraschiv and Stefan Ruseti, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Ruseti, S., Paraschiv, I., Dascalu, M. et al. Automated Pipeline for Multi-lingual Automated Essay Scoring with ReaderBench. Int J Artif Intell Educ (2024). https://doi.org/10.1007/s40593-024-00402-4

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Accepted : 13 March 2024

Published : 01 April 2024

DOI : https://doi.org/10.1007/s40593-024-00402-4

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• Automated essay scoring
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• Linguistic features
• Transformer-based models

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