loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Abbirah Ahmed ; Arash Joorabchi and Martin Hayes

Affiliation: Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland

Keyword(s): Automatic Short Answer Grading, Deep Learning, Natural Language Processing, Blended Learning, Automated Assessment

Abstract: The recent increase in the number of courses that are delivered in a blended fashion, before the effect of the pandemic has even been considered, has led to a concurrent interest in the question of how appropriate or useful automated assessment can be in such a setting. In this paper, we consider the case of automated short answer grading (ASAG), i.e., the evaluation of student answers that are strictly limited in terms of length using machine learning and in particular deep learning methods. Although ASAG has been studied for over 50 years, it is still one of the most active areas of NLP research as it represents a starting point for the possible consideration of more open ended or conversational answering. The availability of good training data, including inter alia, labelled and domain-specific information is a key challenge for ASAG. This paper reviews deep learning approaches to this question. In particular, deep learning models, dataset curation, and evaluation metrics for ASAG tasks are considered in some detail. Finally, this study considers the development of guidelines for educators to improve the applicability of ASAG research. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.237.0.109

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ahmed, A.; Joorabchi, A. and Hayes, M. (2022). On Deep Learning Approaches to Automated Assessment: Strategies for Short Answer Grading. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-562-3; ISSN 2184-5026, pages 85-94. DOI: 10.5220/0011082100003182

@conference{csedu22,
author={Abbirah Ahmed. and Arash Joorabchi. and Martin Hayes.},
title={On Deep Learning Approaches to Automated Assessment: Strategies for Short Answer Grading},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2022},
pages={85-94},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011082100003182},
isbn={978-989-758-562-3},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - On Deep Learning Approaches to Automated Assessment: Strategies for Short Answer Grading
SN - 978-989-758-562-3
IS - 2184-5026
AU - Ahmed, A.
AU - Joorabchi, A.
AU - Hayes, M.
PY - 2022
SP - 85
EP - 94
DO - 10.5220/0011082100003182