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ERIC Number: EJ1125868
Record Type: Journal
Publication Date: 2017
Pages: 14
Abstractor: As Provided
ISSN: EISSN-1436-4522
Comment Data Mining to Estimate Student Performance Considering Consecutive Lessons
Sorour, Shaymaa E.; Goda, Kazumasa; Mine, Tsunenori
Educational Technology & Society, v20 n1 p73-86 2017
The purpose of this study is to examine different formats of comment data to predict student performance. Having students write comment data after every lesson can reflect students' learning attitudes, tendencies and learning activities involved with the lesson. In this research, Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are employed to predict student grades in each lesson. In order to obtain further improvement of prediction results, a majority vote method is applied to the predicted results obtained in consecutive lessons. The research findings show that our proposed method continuously tracked student learning situations and improved prediction performance of final student grades.
International Forum of Educational Technology & Society. Athabasca University, School of Computing & Information Systems, 1 University Drive, Athabasca, AB T9S 3A3, Canada. Tel: 780-675-6812; Fax: 780-675-6973; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Japan
Grant or Contract Numbers: N/A