TY - GEN
T1 - On Deep Learning Approaches to Automated Assessment
T2 - 14th International Conference on Computer Supported Education, CSEDU 2022
AU - Ahmed, Abbirah
AU - Joorabchi, Arash
AU - Hayes, Martin J.
N1 - Publisher Copyright:
Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Automated Assessment
KW - Automatic Short Answer Grading
KW - Blended Learning
KW - Deep Learning
KW - Natural Language Processing
UR - http://www.scopus.com/inward/record.url?scp=85140904376&partnerID=8YFLogxK
U2 - 10.5220/0011082100003182
DO - 10.5220/0011082100003182
M3 - Conference contribution
AN - SCOPUS:85140904376
T3 - International Conference on Computer Supported Education, CSEDU - Proceedings
SP - 85
EP - 94
BT - Proceedings of the 14th International Conference on Computer Supported Education - Volume 2, CSEDU 2022
A2 - Cukurova, Mutlu
A2 - Rummel, Nikol
A2 - Gillet, Denis
A2 - McLaren, Bruce
A2 - Uhomoibhi, James
PB - Science and Technology Publications, Lda
Y2 - 22 April 2022 through 24 April 2022
ER -