Student performance estimation based on topic models considering a range of lessons

Shaymaa E. Sorour, Kazumasa Goda, Tsunenori Mine

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

2 Citations (Scopus)

Abstract

This paper proposes a prediction framework for student performance based on comment data mining. Given the comments containing multiple topics, we seek to discover the topics that help to predict final student grades as their performance. To this end, the paper proposes methods that analyze students’ comments by two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA). The methods employ Support Vector Machine (SVM) to generate prediction models of final student grades. In addition, Considering the student grades predicted in a range of lessons can deal with prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
EditorsCristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
PublisherSpringer Verlag
Pages790-793
Number of pages4
ISBN (Print)9783319197722
DOIs
Publication statusPublished - Jan 1 2015
Event17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain
Duration: Jun 22 2015Jun 26 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9112
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Artificial Intelligence in Education, AIED 2015
CountrySpain
CityMadrid
Period6/22/156/26/15

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sorour, S. E., Goda, K., & Mine, T. (2015). Student performance estimation based on topic models considering a range of lessons. In C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings (pp. 790-793). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112). Springer Verlag. https://doi.org/10.1007/978-3-319-19773-9_117