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Clement, Benjamin; Oudeyer, Pierre-Yves; Lopes, Manuel – International Educational Data Mining Society, 2016
Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally…
Descriptors: Intelligent Tutoring Systems, Markov Processes, Models, Teaching Methods
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Lee, Hee Seung; Betts, Shawn; Anderson, John R. – Cognitive Science, 2016
Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem…
Descriptors: Problem Solving, Hypothesis Testing, Experiments, Cognitive Processes
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Anderson, John R.; Fincham, Jon M. – Cognitive Science, 2014
Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel…
Descriptors: Cognitive Structures, Pattern Recognition, Markov Processes, Cognitive Processes
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis