Automated essay scoring machine learning

Automated essay scoring machine learning

tomated essay scoring. 3 Automated Essay Scoring by Maximizing Human-machine Agreement The main work-ow of our proposed approach is as follows. Firstly, a set of essays rated by profes-sional human raters are gathered for the training. A listwise learning to rank algorithm learns a ranking model or function using this set of human rated es- 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 Abstract - Automated Essay Scoring (AES) is a great research area to analyze the human expertise. AES is one of the most challenging activities in Natural Language Processing (NLP). It makes use of NLP and Machine Learning (ML) techniques to predict the score and match with the human like grading system. The very first model was PEG

Learning Analytics for Supporting Individualization: Data-informed Adaptation of Learning View all 4 Articles, automated essay scoring machine learning. 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 automated essay scoring machine learning first layer of explanation underlying the prediction of holistic scores.

Consequently, the AES black box has remained impenetrable, automated essay scoring machine learning. Although several algorithms from Explainable Artificial Intelligence have recently been published, automated essay scoring machine learning, 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.

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. 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 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, automated essay scoring machine learning, this study reports a a list of features that are important for a specific essay locallyb 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 automated essay scoring machine learning 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. 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, ; Murdoch et al. At its turn, AES may automated essay scoring machine learning from its own set of biases e.

This required changing the perception that AES is merely a machine learning and feature engineering task Madnani et al. 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, automated essay scoring machine learning 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, ; West-Smith et al.

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, automated essay scoring machine learning. It has been previously recognized that divergence of opinion among human and machine graders has been only investigated superficially Reinertsen, 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, 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, automated essay scoring machine learning, they did not provide any feedback as to how to improve those suboptimal essay segments Mizumoto et al. Related to this study and the work of Kumar and Boulanger is Revision Assistant, a commercial AES system developed by Turnitin Woods et al.

The implementation of Revision Assistant moved away from the traditional approach to AES, automated essay scoring machine learning, which consists in automated essay scoring machine learning a limited set of features engineered by human experts representing only high-level characteristics of essays.

Like this study, automated essay scoring machine learning, 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.

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 in his doctoral dissertation leverages a recurrent neural network to improve accuracy in predicting holistic scores, implement rubric scoring i. Interestingly, Taghipour compared the performance of his AES system against other AES systems using automated essay scoring machine learning ASAP corpora, automated essay scoring machine learning, 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. Despite their numbers, rubrics e.

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, automated essay scoring machine learning, 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, automated essay scoring machine learning, the AES used in this study and developed by Kumar and Boulanger is unique in that it uses both deep learning multi-layer perceptron neural network and a huge pool of linguistic indicesautomated essay scoring machine learning, 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, 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. 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. 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.

This study showcases the application of the PDR framework Murdoch et al, automated essay scoring machine learning.

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, automated essay scoring machine learning, two key components to generate trustworthy interpretations Murdoch et al. 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. 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 automated essay scoring machine learning the intended stakeholders.

As previously mentioned, this paper reuses the AES system developed by Kumar and Boulanger 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 words. Students were asked to write a story about patience. 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, and Kumar and Boulanger 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,is determined and justified through its four rubrics. Rubric scores, in turn, automated essay scoring machine learning, are investigated to highlight the writing features that play an important role within each rubric Kumar and Boulanger, This paper is a continuation of these previous works by adding the following link to the AES chain: holistic score, rubric scores, automated essay scoring machine learning 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. The AES system is fed by linguistic indices quantitatively measured by the Suite of Automatic Linguistic Analysis Tools 2 SALATwhich assess aspects of grammar and mechanics, sentiment analysis and cognition, text cohesion, lexical diversity, lexical sophistication, and syntactic sophistication and complexity Kumar and Boulanger, 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.

However, to facilitate this process, this study opted for a semi-automatic strategy that consisted of both filter and embedded methods. While the texts of all 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 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. Thirdly, the last feature of any pair of features having an absolute Pearson correlation coefficient greater than 0. After the application of these filter methods, the number of features was reduced from to Finally, the Lasso and Ridge regression regularization methods whose combination is also called ElasticNet were applied during the training of the rubric scoring automated essay scoring machine learning. Lasso is responsible for pruning further features, while Ridge regression is entrusted with eliminating multicollinearity among features.

First, a study should list the hyperparameters it is going to investigate by testing for various values of each hyperparameter. For example, automated essay scoring machine learning, 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. Of particular interest to this study is the neural network itself, that is, how many hidden layers should a neural network have and how automated essay scoring machine learning 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.

Table 1. Hyperparameter subspace investigated in this article along with best hyperparameter values per neural network architecture. No validation set was put aside; 5-fold cross-validation was rather used for hyperparameter optimization. Table 1 delineates the hyperparameter subspace from which different combinations of hyperparameter values were randomly selected out of a subspace of 86, 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, toparameters 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 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.

Table 2 delineates the performance of predictive models trained previously by Kumar and Boulanger 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. 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 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. Even though the predicted holistic score might be accurate, the rubric scores could all be inaccurate. Similarity or distance metrics e. They also supply automated essay scoring machine learning information about the study setting, essay datasets, rubrics, features, natural language processing NLP tools, model training, and evaluation against human performance.

EEL6825 - Automated Essay Grading

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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 machine learning consisted of scored literary analysis essays written in an English Literature course, each essay analyzing a theme in a given literary work. To train the automated scoring model, LightSide software was used. First, textual features were extracted and filtered. Then, Logistic Regression, SMO, SVO, Logistic Tree and Naïve Automated Essay Scoring (AES) is defined as the computer technology that evaluates and scores the written prose (Shermis & Barrera, ; Shermis & Burstein, ; Shermis, Raymat, & Barrera, ). AES systems are mainly used to overcome time, cost, reliability