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Automated Essay Scoring Using Deep Learning Algorithms

Automated Essay Scoring Using Deep Learning Algorithms
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Author(s): Jinnie Shin (University of Alberta, Canada), Qi Guo (Medical Council of Canada, Canada) and Mark J. Gierl (University of Alberta, Canada)
Copyright: 2021
Pages: 11
Source title: Handbook of Research on Modern Educational Technologies, Applications, and Management
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3476-2.ch003

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Abstract

The recent transition from paper to digitally based assessment has brought many positive changes in educational testing. For example, many high-stakes exams have started implementing essay-type questions because they allow students to creatively express their understanding with their own words. To reduce the burden of scoring these items, the implementation of automated essay scoring (AES) systems have gained more attention. However, despite some of the successful demonstrations, AES still encountered many criticisms from practitioners. Such concerns often include prediction accuracy and interpretability of the scoring algorithms. Hence, overcoming these challenges is critical for AES to be widely adopted in the field. The purpose of this chapter is to introduce deep learning AES models and to describe how certain aspects of the models can be used to overcome the challenges of prediction accuracy and interpretability of the scoring algorithms.

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