Prediction of Students’ Grades Based on Free-Style Comments Data

  • Shaymaa E. Sorour
  • Tsunenori Mine
  • Kazumasa Goda
  • Sachio Hirokawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8613)


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.


Comments Data Overlap method Similarity measuring 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shaymaa E. Sorour
    • 1
    • 2
  • Tsunenori Mine
    • 2
  • Kazumasa Goda
    • 3
  • Sachio Hirokawa
    • 4
  1. 1.Kafr Elsheik UniversityKafr ElsheikhEgypt
  2. 2.Kyushu UniversityFukuokaJapan
  3. 3.Kyushu Institute of Information ScienceFukuokaJapan
  4. 4.Kyushu UniversityFukuokaJapan

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