Evaluation of effectiveness of time-series comments by using machine learning techniques

Shaymaa E. Sorour, Kazumasa Goda, Tsunenori Mine

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Understanding individual students more deeply in the class is the most vital role in educational situations. Using comment data written by students after each lesson helps in the understanding of their learning attitudes and situations. They can be a powerful source of data for all forms of assessment. The PCN method categorizes the comments into three items: P (Previous learning activity), C (Current learning activity), and N (Next learning activity plan). The objective of this paper is to investigate how the three time-series items: P, C, and N, and the difficulty of a subject affect the prediction results of final student grades using two types of machine learning techniques: Support Vector Machine (SVM) and Artificial Neural Network (ANN). The experiment results indicate that the students described their current activities (C-comment) in more detail than previous and next activities (P-and N-comments); this tendency is reflected in prediction accuracy and F-measure of their grades.

Original languageEnglish
Pages (from-to)784-794
Number of pages11
JournalJournal of information processing
Issue number6
Publication statusPublished - Nov 15 2015

All Science Journal Classification (ASJC) codes

  • General Computer Science


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