Comments data mining for evaluating student's performance

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

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

4 Citations (Scopus)

Abstract

The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences between the comments in the two classes. The proposed methods basically employ latent semantic analysis (LSA) and two types of machine learning technique: SVM (support vector machine) and ANN (artificial neural network) for predicting students' final results in four grades of S, A, B and C. Moreover, an overlap method was proposed to improve the accuracy prediction results, the method allows to accept two grades for one mark to get the correct relation between LSA results and students' grades. The proposed methods achieve 50.7% and 48.7% prediction accuracy of students' grades by SVM and ANN, respectively. To this end, the results of this study reported models of students' academic performance predictors that are valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities.

Original languageEnglish
Title of host publicationProceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-30
Number of pages6
ISBN (Electronic)9781479941735
DOIs
Publication statusPublished - Sep 29 2014
Event3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014 - Kitakyushu, Japan
Duration: Aug 31 2014Sep 4 2014

Publication series

NameProceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014

Other

Other3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014
CountryJapan
CityKitakyushu
Period8/31/149/4/14

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Sorour, S. E., Mine, T., Godaz, K., & Hirokawa, S. (2014). Comments data mining for evaluating student's performance. In Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014 (pp. 25-30). [6913261] (Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2014.17