Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
ACM Computing Surveys (CSUR)
Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
Pattern discovery in sequences under a Markov assumption
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining tasks and methods: Clustering: conceptual clustering
Handbook of data mining and knowledge discovery
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
A bayesian approach to temporal data clustering using the hidden markov model methodology
A bayesian approach to temporal data clustering using the hidden markov model methodology
Adaptive teaching strategy for online learning
Proceedings of the 10th international conference on Intelligent user interfaces
A study of the effects of bias in criterion functions for temporal data clustering
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
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This paper discusses and evaluates a modeling approach for student online learning. It is developed as a key component of an adaptive online tutoring system, AToL. At the beginning of the learning process, classification of student learning style is applied based on each student's responses to a few learning related questions. Clustering is then used to model student behavior for each learning style using data collected as the students interact with the system. A Bayesian Markov chain based temporal data clustering method is developed for this step. We evaluated the student modeling component of the AToL system using data collected from the CS-I students who participated in the experiments in Spring 05. We compared the quality of the models built using these two approaches. We also compared the models built for the same group of students when learning different concepts.