Correlation of grade prediction performance with characteristics of lesson subject

Shaymaa E. Sorour, Jingyi Luo, Kazumasa Goda, Tsunenori Mine

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

6 Citations (Scopus)

Abstract

Learning analytics is valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities. Analyzing comment data written by students after each lesson helps to grasp their learning attitudes and situations. They can be a powerful source of data for all forms of assessment. In the current study, we break down student comments into different topics by employing two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA), to discover the topics that help to predict final student grades as their performance. The objectives of this paper are twofold: First, determine how the three time-series items: P-, C- and N-comments and the difficulty of a subject affect the prediction results of final student grades. Second, evaluate the reliability of predicting student grades by considering the differences between prediction results of two consecutive lessons. The results obtained can help to understand student behavior during the period of the semester, grasp prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.

Original languageEnglish
Title of host publicationProceedings - IEEE 15th International Conference on Advanced Learning Technologies
Subtitle of host publicationAdvanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015
EditorsNian-Shing Chen, Tzu-Chien Liu, Kinshuk, Ronghuai Huang, Gwo-Jen Hwang, Demetrios G. Sampson, Chin-Chung Tsai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages247-249
Number of pages3
ISBN (Electronic)9781467373333
DOIs
Publication statusPublished - Sep 14 2015
Event15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015 - Hualien, Taiwan, Province of China
Duration: Jul 6 2015Jul 9 2015

Publication series

NameProceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015

Other

Other15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015
CountryTaiwan, Province of China
CityHualien
Period7/6/157/9/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Experimental and Cognitive Psychology
  • Computer Networks and Communications
  • Human-Computer Interaction
  • Education

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  • Cite this

    Sorour, S. E., Luo, J., Goda, K., & Mine, T. (2015). Correlation of grade prediction performance with characteristics of lesson subject. In N-S. Chen, T-C. Liu, Kinshuk, R. Huang, G-J. Hwang, D. G. Sampson, & C-C. Tsai (Eds.), Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015 (pp. 247-249). [7265316] (Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICALT.2015.24