Multivariate Cognitive Response Framework for Student Performance Prediction on MOOC

Based on student's cognitive structure, the cognitive diagnostic models (CDMs) can reveal the potential relationships among the student's knowledge level, test item features and the corresponding item scores, and then predict each student's future performance. However, due to the simplistic prior information and deficient cognitive mechanism, most of the existing CDMs have limited prediction performance. To address the issues, we propose the multivariate cognitive response framework (MvCRF). We firstly collect student's learning activity logs to calculate the corresponding effort trait. Considering both student's ability trait and effort trait, MvCRF then introduces the compensation mechanism to calculate student's knowledge level. In addition, we introduce not only the slip and guessing parameters in prediction but also the skill weakness parameter related with the student's knowledge level and the importance of each skill on solving specific item. Experimental results on both simulation study and real-data application on MOOC demonstrate that MvCRF achieves better prediction performance, robustness and interpretability than the baseline CDMs.