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Author: Rumiko Azuma

Affiliation: College of Commerce, Nihon University, Setagaya-ku, Tokyo, Japan

Keyword(s): Learning Analytics, Machine Learning, Text Mining, Educational Support, Reflection Sheet.

Abstract: In recent years, schools and universities have become more focused on how to allow learners to learn successfully, and it has become an expectation to design instruction in a way that takes into account the individual differences of learners. Accordingly, the purpose of this study is to predict, at an earlier stage in a course, which students are likely to fail, so that adequate support can be provided for them. We proposed a new approach to identify such students using free-response self-reflection sheets. This method uses the unrestricted comments from the students to create a comment vector that can be used to predict who are likely to fail the course. Subsequently, we conducted experiments to verify the effectiveness of this prediction. In comparison to methods used in existing research which predict potential failures using quiz scores and the students’ subjective level of understanding, our proposed method was able to improve the prediction performance. In addition, when cumula tive data after several sessions were used to predict which students were likely to fail, the predictions made by the support vector machine (SVM) algorithm showed a consistent prediction performance, and the prediction accuracy was higher than that of other algorithms. (More)

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Paper citation in several formats:
Azuma, R. (2021). Effectiveness of Comments on Self-reflection Sheet in Predicting Student Performance. In Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - ICORES, ISBN 978-989-758-485-5; ISSN 2184-4372, pages 394-400. DOI: 10.5220/0010197503940400

@conference{icores21,
author={Rumiko Azuma.},
title={Effectiveness of Comments on Self-reflection Sheet in Predicting Student Performance},
booktitle={Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - ICORES,},
year={2021},
pages={394-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010197503940400},
isbn={978-989-758-485-5},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - ICORES,
TI - Effectiveness of Comments on Self-reflection Sheet in Predicting Student Performance
SN - 978-989-758-485-5
IS - 2184-4372
AU - Azuma, R.
PY - 2021
SP - 394
EP - 400
DO - 10.5220/0010197503940400

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