Probabilistic latent class models for predicting student performance

Authors:
Suleyman Cetintas;Luo Si;Yan Ping Xin;Ron Tzur
Affiliations:
Yahoo! Labs, Sunnyvale, CA, USA;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;University of Colorado Denver, Denver, CO, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

Predicting student performance is an important task for many core problems in intelligent tutoring systems. This paper proposes a set of novel probabilistic latent class models for the task. The most effective probabilistic model utilizes all available information about the educational content and users/students to jointly identify hidden classes of students and educational content that share similar characteristics, and to learn a specialized and fine-grained regression model for each latent educational content and student class. Experiments carried out on large-scale real-world datasets demonstrate the advantages of the proposed probabilistic latent class models.