Abstract
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This paper presents a predictive student actionmodel, which uses student logs generated by a 3D virtual environment for procedural training to elaborate summarized information. This model can predict the most common behaviors by con- sidering the sequences of more frequent actions, which is useful to anticipate common student? errors. These logs are clustered based on the number of errors made by each stu- dent and the total time that each student spent to complete the entire practice. Next, for each cluster an extended au- tomata is created, which allows us to generate predictions more reliable to each student type. In turn, the action pre- diction based on this model helps an intelligent tutoring sys- tem to generate students? feedback proactively. | |
International
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Si |
Congress
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Educational Data Mining 2015: 8th International Conference on Educational Data Mining |
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970 |
Place
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MADRID |
Reviewers
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Si |
ISBN/ISSN
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978-84-606-9425-0 |
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Start Date
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26/06/2015 |
End Date
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29/06/2015 |
From page
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614 |
To page
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615 |
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A Model for Student Action Prediction in 3D Virtual Environments for Procedural Training |