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Abstract
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| This paper presents a way to predict student actions, by us- ing student logs generated by a 3D virtual environment for procedural training. Each student log is categorized in a cluster based on the number of errors and the total time spent to complete the entire practice. For each cluster an extended automata is created, which allows us to gen- erate more reliable predictions according to each student type. States of this extended automata represent the effect of a student correct or failed action. The most common behaviors can be predicted considering the sequences of more frequent actions. This is useful to anticipate com- mon student errors, and this can help an Intelligent Tutoring System to generate feedback proactively. Key | |
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International
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Si |
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10.1007/978-3-319-28883-3 |
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Book Edition
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1 |
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Book Publishing
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Springer |
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ISBN
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978-3-319-28882-6 |
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Series
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Book title
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E-Learning, E-Education, and Online Training |
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From page
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200 |
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To page
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207 |