Prediction of students' grades based on free-style comments data

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

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

2 Citations (Scopus)

Abstract

In this paper we propose a new approach based on text mining technique to predict student's performance using LSA (latent semantic analysis) and K-means clustering method. The present study uses free style comments written by students after each lesson. Since the potentials of these comments can reflect students' learning attitudes, understanding and difficulties to the lessons, they enable teachers to grasp the tendencies of students' learning activities.To improve this basic approach, overlap method and similarity measuring technique are proposed. We conducted experiments to validate our proposed methods. The experimental results illustrated that prediction accuracy was 73.6% after applying the overlap method and that was 78.5% by adding the similarity measuring.

Original languageEnglish
Title of host publicationAdvances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings
PublisherSpringer Verlag
Pages142-151
Number of pages10
ISBN (Print)9783319096346
DOIs
Publication statusPublished - Jan 1 2014
Event13th International Conference on Advances in Web-Based Learning, ICWL 2014 - Tallinn, Estonia
Duration: Aug 14 2014Aug 17 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8613 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Advances in Web-Based Learning, ICWL 2014
CountryEstonia
CityTallinn
Period8/14/148/17/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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