Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Coalescing individual and collaborative learning to model user linguistic competences
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Electronic Commerce Research and Applications
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ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Awareness and collaboration in the ihelp courses content management system
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
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In this paper, we examine a challenge that arises in the application of peer-based tutoring: coping with inappropriate advice from peers. We examine an environment where students are presented with those learning objects predicted to improve their learning (on the basis of the success of previous, like-minded students) but where peers can additionally inject annotations. To avoid presenting annotations that would detract from student learning (e.g. those found confusing by other students) we integrate trust modeling, to detect over time the reputation of the annotation (as voted by previous students) and the reputability of the annotator. We empirically demonstrate, through simulation, that even when the environment is populated with a large number of poor annotations, our algorithm for directing the learning of the students is effective, confirming the value of our proposed approach for student modeling. In addition, the research introduces a valuable integration of trust modeling into educational applications.