High-Level Student Modeling with Machine Learning
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Inducing High-Level Behaviors from Problem-Solving Traces Using Machine-Learning Tools
IEEE Intelligent Systems
Collaborative Information Filtering: A Review and an Educational Application
International Journal of Artificial Intelligence in Education
Using Collaborative Filtering Algorithms as eLearning Tools
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal collaborative filtering with adaptive neighbourhoods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Toward legal argument instruction with graph grammars and collaborative filtering techniques
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Evaluating collaborative filtering recommendations inside large learning object repositories
Information Processing and Management: an International Journal
Probabilistic latent class models for predicting student performance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Collaborative filtering (CF) is a technique that utilizes how users are associated with items in a target application and predicts the utility of items for a particular user. Temporal collaborative filtering (temporal CF) is a time-sensitive CF approach that considers the change in user-item interactions over time. Despite its capability to deal with dynamic educational applications with rapidly changing user-item interactions, there is no prior research of temporal CF on educational tasks. This paper proposes a temporal CF approach to automatically predict the correctness of students' problem solving in an intelligent math tutoring system. Unlike traditional user-item interactions, a student may work on the same problem multiple times, and there are usually multiple interactions for a student-problem pair. The proposed temporal CF approach effectively utilizes information coming from multiple interactions and is compared to i) a traditional CF approach, ii) a temporal CF approach that uses a sliding-time-window but ignores old data and multiple interactions and iii) a combined temporal CF approach that uses a sliding-time-window together with multiple interactions. An extensive set of experiment results show that using multiple-interactions significantly improves the prediction accuracy while using sliding-time-windows doesn't make a significant difference.