A machine learning framework for an expert tutor construction

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Proceedings - International Conference on Computers in Education, ICCE 2002

First Page

670

Last Page

674

Publication Date

1-1-2002

Abstract

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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Digitial Object Identifier (DOI)

10.1109/CIE.2002.1186038

Disciplines

Computer Sciences

Keywords

Intelligent tutoring systems; Expert systems (Computer science)

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