Prediction Models for Automatic Assessment to Students’ Freely-written Comments

Jihed Makhlouf, Tsunenori Mine

Abstract

Tracking students’ learning situations is taking a fundamental place in educational institutions. Thanks to the advances in educational technology, we are able to gather more and more data about students using educational software systems. Analyzing such data helped researchers build models that could predict students’ behaviors and scores. However, in classroom-based settings, teachers and professors find difficulties to perfectly grasp all their students’ learning attitudes. In an approach to address this issue, we asked the students to give freely-written comments answering predefined questions about their learning experience. Thereafter, professors read these comments and give feedback to each student. Nonetheless, professors find themselves overwhelmed by the number of comments which make this approach not scalable to multiple classes for the same professor. In this paper, we address this issue by building a model that can automatically assess the students’ comments. We use two different approaches. In the first approach, we treat all student comments the same way, regardless of which question they are related to. The second approach consists of building different individual models that analyze students’ comments depending on the question. Experimental results show that the prediction accuracy of assessment to student comments can reach 74%.

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Paper Citation


in Harvard Style

Makhlouf J. and Mine T. (2020). Prediction Models for Automatic Assessment to Students’ Freely-written Comments.In Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-417-6, pages 77-86. DOI: 10.5220/0009580300770086


in Bibtex Style

@conference{csedu20,
author={Jihed Makhlouf and Tsunenori Mine},
title={Prediction Models for Automatic Assessment to Students’ Freely-written Comments},
booktitle={Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2020},
pages={77-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009580300770086},
isbn={978-989-758-417-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Prediction Models for Automatic Assessment to Students’ Freely-written Comments
SN - 978-989-758-417-6
AU - Makhlouf J.
AU - Mine T.
PY - 2020
SP - 77
EP - 86
DO - 10.5220/0009580300770086