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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Trait-based Deep Learning Automated Essay Scoring System with Adaptive Feedback

Author 1: Mohamed A. Hussein
Author 2: Hesham A. Hassan
Author 3: Mohammad Nassef

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110538

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

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Abstract: Numerous Automated Essay Scoring (AES) systems have been developed over the past years. Recent advances in deep learning have shown that applying neural network approaches to AES systems has accomplished state-of-the-art solutions. Most neural-based AES systems assign an overall score to given essays, even if they depend on analytical rubrics/traits. The trait evaluation/scoring helps to identify learners’ levels of performance. Besides, providing feedback to learners about their writing performance is as important as assessing their level. Producing adaptive feedback to the learners requires identifying the strengths/weaknesses and the magnitude of influence of each trait. In this paper, we develop a framework that strengthens the validity and enhances the accuracy of a baseline neural-based AES model with respect to traits evaluation/scoring. We extend the model to present a method based on essay traits prediction to give trait-specific adaptive feedback. We explored multiple deep learning models for the automatic essay scoring task, and we performed several analyses to get some indicators from these models. The results show that Long Short-Term Memory (LSTM) based system outperformed the baseline study by 4.6% in terms of quadratic weighted Kappa (QWK). Moreover, the prediction of the traits scores enhance the efficiency of the prediction of the overall score. Our extended model is used in the iAssistant, an educational module that provides trait-specific adaptive feedback to learners.

Keywords: AES system; trait evaluation; adaptive feedback; deep learning; neural networks; ASAP

Mohamed A. Hussein, Hesham A. Hassan and Mohammad Nassef, “A Trait-based Deep Learning Automated Essay Scoring System with Adaptive Feedback” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110538

@article{Hussein2020,
title = {A Trait-based Deep Learning Automated Essay Scoring System with Adaptive Feedback},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110538},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110538},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Mohamed A. Hussein and Hesham A. Hassan and Mohammad Nassef}
}


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