Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
An iterative design methodology for user-friendly natural language office information applications
ACM Transactions on Information Systems (TOIS)
Multi-Agent Multi-User Modeling in I-Help
User Modeling and User-Adapted Interaction
An empirical methodology for writing user-friendly natural language computer applications
CHI '83 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Wizard of Oz prototyping of computer vision based action games for children
Proceedings of the 2004 conference on Interaction design and children: building a community
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Creating cognitive tutors for collaborative learning: steps toward realization
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
UM '07 Proceedings of the 11th international conference on User Modeling
Toward Social Learning Environments
IEEE Transactions on Learning Technologies
Lifelong Learner Modeling for Lifelong Personalized Pervasive Learning
IEEE Transactions on Learning Technologies
Content Matters: An Investigation of Feedback Categories within an ITS
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
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 present an algorithm for reasoning about the sequencing of content for students in an intelligent tutoring system, influenced by McCalla's ecological approach. We record with each learning object those students who experienced the object, together with their initial and final states of knowledge, and then use these interactions to reason about the most effective lesson to show future students based on their similarity to previous students. We validate our approach through a novel method of validation, providing details of the model of learning used in the simulation and the results obtained in order to demonstrate the value of our model. Beyond confirmation through simulations of student learning, we report on a study with human users and expand on a previous pilot study. We demonstrate the effectiveness of our algorithms for selection of learning objects to solidify the overall defence of our approach.