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Probabilistic Latent Class Models for Predicting Student Performance

Publication
Aug 30, 2013
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.

  • Proceedings of the 22nd International ACM CIKM Conference on Information and Knowledge Management

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