Correlation of topic model and student grades using comment data mining

Shaymaa E. Sorour, Kazumasa Goda, Tsunenori Mine

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

12 被引用数 (Scopus)

抄録

Assessment of learning progress and learning gain play a pivotal role in education fields. New technologies like comment data mining promote the use of new types of contents; student comments highly reflect student learning attitudes and activities compared to more traditional methods and they can be a powerful source of data for all forms of assessment. A teacher just asks students after every lesson to freely describe and write about their learning situations and behaviors. This paper proposes new methods based on a statistical latent class "Topics" for the task of student grade prediction; our methods convert student comments using latent semantic analysis (LSA) and probabilistic latent semantic analysis (PLSA), and generate prediction models using support vector machine (SVM) and artificial neural network (ANN) to predict student final grades. The experimental results show that our methods can accurately predict student grades based on comment data.

本文言語英語
ホスト出版物のタイトルSIGCSE 2015 - Proceedings of the 46th ACM Technical Symposium on Computer Science Education
編集者Adrienne Decker, Kurt Eiselt, Jodi Tims, Carl Alphonce
出版社Association for Computing Machinery
ページ441-446
ページ数6
ISBN(電子版)9781450329668
DOI
出版ステータス出版済み - 2月 24 2015
イベント46th SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2015 - Kansas City, 米国
継続期間: 3月 4 20153月 7 2015

出版物シリーズ

名前SIGCSE 2015 - Proceedings of the 46th ACM Technical Symposium on Computer Science Education

その他

その他46th SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2015
国/地域米国
CityKansas City
Period3/4/153/7/15

!!!All Science Journal Classification (ASJC) codes

  • 教育
  • コンピュータ サイエンス(その他)

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