Estimation of Student Performance by Considering Consecutive Lessons

Shaymaa E. Sorour, Kazumasa Goda, Tsunenori Mine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Examining student learning behavior is one of the crucial educational issues. In this paper, we propose a new method to predict student performance by using comment data mining. A teacher just asks students after every lesson to freely describe and write about their learning situations, attitudes, tendencies, and behaviors. The method employs Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) to predict student grades in each lesson. In order to obtain further improvement of prediction results, we apply a majority vote method to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. Also, we evaluate the reliability of the predicted student grades to know when we can rely prediction results of student grade during the period of the semester. The experiment results show that our proposed method continuously tracked student learning situation and improved prediction performance of final student grades compared to Probabilistic Latent Semantic Analysis (PLSA) and Latent Semantic Analysis (LSA) models. Also, considering the differences of prediction results in the two consecutive lessons helps to evaluate the reliability of the predicted results.

Original languageEnglish
Title of host publicationProceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015
EditorsSachio Hirokawa, Kiyota Hashimoto, Tokuro Matsuo, Tsunenori Mine
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages121-126
Number of pages6
ISBN (Electronic)9781479999583
DOIs
Publication statusPublished - Jan 6 2016
Event4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015 - Okayama, Japan
Duration: Jul 12 2015Jul 16 2015

Publication series

NameProceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015

Other

Other4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015
CountryJapan
CityOkayama
Period7/12/157/16/15

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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