Prediction of students' grades based on free-style comments data

Shaymaa E. Sorour, Tsunenori Mine, Kazumasa Goda, Sachio Hirokawa

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)

抄録

In this paper we propose a new approach based on text mining technique to predict student's performance using LSA (latent semantic analysis) and K-means clustering method. The present study uses free style comments written by students after each lesson. Since the potentials of these comments can reflect students' learning attitudes, understanding and difficulties to the lessons, they enable teachers to grasp the tendencies of students' learning activities.To improve this basic approach, overlap method and similarity measuring technique are proposed. We conducted experiments to validate our proposed methods. The experimental results illustrated that prediction accuracy was 73.6% after applying the overlap method and that was 78.5% by adding the similarity measuring.

本文言語英語
ホスト出版物のタイトルAdvances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings
出版社Springer Verlag
ページ142-151
ページ数10
ISBN(印刷版)9783319096346
DOI
出版ステータス出版済み - 2014
イベント13th International Conference on Advances in Web-Based Learning, ICWL 2014 - Tallinn, エストニア
継続期間: 8月 14 20148月 17 2014

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8613 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他13th International Conference on Advances in Web-Based Learning, ICWL 2014
国/地域エストニア
CityTallinn
Period8/14/148/17/14

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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