Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
Dynamically adjusting categories to accommodate changing contexts
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Procedural help in Andes: generating hints using a Bayesian network student model
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Growth and maturity of intelligent tutoring systems: a status report
Smart machines in education
Using Decision Trees for Agent Modeling: Improving Prediction Performance
User Modeling and User-Adapted Interaction
Modeling Student Knowledge: Cognitive Tutors in High School and College
User Modeling and User-Adapted Interaction
Conceptual Clustering, Categorization, and Polymorphy
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Clustering the Users of Large Web Sites into Communities
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
High-Level Student Modeling with Machine Learning
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
CLARISSE: A Machine Learning Tool to Initialize Student Models
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
A unified framework for model-based clustering
The Journal of Machine Learning Research
A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems
User Modeling and User-Adapted Interaction
Evaluating the REDEEM Authoring Tool: Can Teachers Create Effective Learning Environments?
International Journal of Artificial Intelligence in Education
Predicting High-level Student Responses Using Conceptual Clustering
Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Teaching-Learning by Means of a Fuzzy-Causal User Model
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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This paper discusses a predictive modeling framework actualized in a learning agent that uses logged tutorial interactions to discover predictive characteristics of students. The agent automatically forms cluster models that are described in terms of student-system interaction attributes, i.e., in terms of the student's knowledge state and behaviour and system's tutoring actions. The agent utilizes the knowledge of its various clusters together with a weighting scheme to learn predictive models of high-level student information, specifically, the time it will take the student to respond to a problem and whether the response is correct, that can be utilized to support individualized adaptation. We investigated utilizing the Self-Organizing Map and AutoClass as clustering algorithms and the naive Bayesian classifier and single layer neural network as weighting algorithms. Empirical results show that by utilizing cluster knowledge the agent's predictions are acceptably strong for response time and accurate at the average for response correctness. Further investigation is needed to validate the scalability of the framework given other datasets and possibly migrate to other approaches that can obtain more meaningful cluster models, detect richer attribute relations, and provide better approximations to further improve prediction of response behaviour for a more informed pedagogical decision-making by the system.