Automated evaluation of student comments on their learning behavior

Kazumasa Goda, Sachio Hirokawa, Tsunenori Mine

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


Learning comments are valuable sources of interpreting student status of understanding. The PCN method introduced in [Gouda2011] analyzes the attitudes of a student from a view point of time series. Each sentence of a comment is manually classified as one of P,C,N or O sentence. P(previous) indicates learning activities before the classtime, C(current) represents understanding or achievements during the classtime, and N(next) means a learning activity plan or goal until next class. The present paper applies SVM(Support Vecotor Machine) to predict the category to which a given sentence belongs. Empirical evaluation using 4,086 sentences was conducted. By selecting feature words of each category, the prediction performance was satisfactory with F-measures 0.8203, 0.7352, 0.8416 and 0.8612 for P,C,N and O respectively.

Original languageEnglish
Pages (from-to)131-140
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8167 LNCS
Publication statusPublished - 2013
Event12th International Conference on Web-based Learning, ICWL 2013 - Kenting, Taiwan, Province of China
Duration: Oct 6 2013Oct 9 2013

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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