Collaborative filtering for student performance prediction

Tak-Lam WONG, Jeff K.T. TANG, Wai-Man PANG, Wai-Shing HO, Chun Hung CHIU, Kin Man POON

Research output: Contribution to conferencePaper

Abstract

Student performance prediction in educational data mining aims at predicting the correctness in performing a given task of a student based on historical data. Accurately predicting the performance allows helpful intervention and useful feedback by instructors to improve students’ learning. This paper presents a framework for tackling the student performance prediction problem using collaborative filtering, which collectively considers behaviour of students as well as the nature and difficulty of questions. Experiments on real-world data of an intelligent tutoring system were conducted to evaluate the effectiveness of our framework.
Original languageEnglish
Publication statusPublished - Jul 2014

Fingerprint

Collaborative filtering
Students
Intelligent systems
Data mining
Feedback
Experiments

Citation

Wong, T.-L., Tang, J. K. T., Pang, W. M., Ho, W. S., Chiu, C. H., & Poon, L. (2014, July). Collaborative filtering for student performance prediction. Paper presented at the Inaugural International Conference on Technology in Education, The Open University of Hong Kong, China.

Keywords

  • Educational data mining
  • Student performance prediction
  • Collaborative filtering