A machine learning framework for an expert tutor construction
This paper discusses a machine learning framework that uses extraction, classification, and generalization techniques to classify students according to their cognitive and behavioral learning patterns and to categorize tutoring strategies of expert human tutors. A great deal of the discussion focuse...
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| Main Authors: | , |
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| Format: | text |
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Animo Repository
2002
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/321 |
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| Institution: | De La Salle University |
| Summary: | This paper discusses a machine learning framework that uses extraction, classification, and generalization techniques to classify students according to their cognitive and behavioral learning patterns and to categorize tutoring strategies of expert human tutors. A great deal of the discussion focuses on the use of reinforcement learning techniques, specifically the ?-greedy and temporal difference TD(0) methods in deriving tutoring policies over a class of students. Future works will deal on incremental learning and modification of learned policies while the tutor performs on-line in real-time, and extracting and learning the way expert tutors execute their tutoring activities. © 2002 IEEE. |
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