Understandable prediction models of student performance using an attribute dictionary

Shaymaa E. Sorour, Shaimaa Abd El Rahman, Samir A. Kahouf, Tsunenori Mine

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

2 Citations (Scopus)

Abstract

This paper proposes a new approach for predicting final student grade with high accuracy. It builds an attribute dictionary (AD) automatically from students’ comments collected after every lesson. Furthermore, it combines white-box models: Decision Tree (DT) and Random Forest (RF), and a black-box model: Support Vector Machine (SVM) to construct an interpretable prediction model and carry out eclectic rule-extraction. First, the AD is built from students’ comments, which are converted to attribute vectors. Second, the output decision is generated by SVM using the attribute vectors in the training phase and then DT and RF are applied to the output decision to extract symbolic rules. Experimental results illustrate the validity of the AD constructed automatically and the superiority of the proposed approach compared to single machine learning techniques: DT, RF and SVM.

Original languageEnglish
Title of host publicationAdvances in Web-Based Learning - ICWL 2016 - 15th International Conference, Proceedings
EditorsUmberto Nanni, Marco Temperini, Marc Spaniol, Dickson K.W. Chiu, Ivana Marenzi
PublisherSpringer Verlag
Pages161-171
Number of pages11
ISBN (Print)9783319474397
DOIs
Publication statusPublished - Jan 1 2016
Event15th International Conference on Advances in Web-Based Learning, ICWL 2016 - Rome, Italy
Duration: Oct 26 2016Oct 29 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10013 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Advances in Web-Based Learning, ICWL 2016
CountryItaly
CityRome
Period10/26/1610/29/16

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sorour, S. E., El Rahman, S. A., Kahouf, S. A., & Mine, T. (2016). Understandable prediction models of student performance using an attribute dictionary. In U. Nanni, M. Temperini, M. Spaniol, D. K. W. Chiu, & I. Marenzi (Eds.), Advances in Web-Based Learning - ICWL 2016 - 15th International Conference, Proceedings (pp. 161-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10013 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-47440-3_18