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

Shaymaa E. Sorour; Kazumasa Goda; Tsunenori Mine

doi:10.1007/978-3-319-19773-9_117

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

Shaymaa E. Sorour, Kazumasa Goda, Tsunenori Mine

Advanced Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
Editors	Cristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
Publisher	Springer Verlag
Pages	790-793
Number of pages	4
ISBN (Print)	9783319197722
DOIs	https://doi.org/10.1007/978-3-319-19773-9_117
Publication status	Published - Jan 1 2015
Event	17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain Duration: Jun 22 2015 → Jun 26 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9112
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	17th International Conference on Artificial Intelligence in Education, AIED 2015
Country	Spain
City	Madrid
Period	6/22/15 → 6/26/15

Fingerprint

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

Student performance estimation based on topic models considering a range of lessons. / Sorour, Shaymaa E.; Goda, Kazumasa; Mine, Tsunenori.

Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings. ed. / Cristina Conati; Neil Heffernan; Antonija Mitrovic; M. Felisa Verdejo. Springer Verlag, 2015. p. 790-793 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sorour, SE, 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9112, Springer Verlag, pp. 790-793, 17th International Conference on Artificial Intelligence in Education, AIED 2015, Madrid, Spain, 6/22/15. https://doi.org/10.1007/978-3-319-19773-9_117

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

Sorour, Shaymaa E. ; Goda, Kazumasa ; Mine, Tsunenori. / Student performance estimation based on topic models considering a range of lessons. Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings. editor / Cristina Conati ; Neil Heffernan ; Antonija Mitrovic ; M. Felisa Verdejo. Springer Verlag, 2015. pp. 790-793 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Access to Document

10.1007/978-3-319-19773-9_117