A predictive model to evaluate student performance

Shaymaa E. Sorour, Tsunenori Mine, Kazumasa Goda, Sachio Hirokawa

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

In this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.

Original languageEnglish
Pages (from-to)192-201
Number of pages10
JournalJournal of information processing
Volume23
Issue number2
DOIs
Publication statusPublished - 2015

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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