Learning Student Models through an Ontology of Learning Strategies

The attempt is to understand errors in the learning behavior of a student. The approach uses an Integrated Machine Learning System (IMLS) sethat uses an ontology of machine learning strategies to decide the appropriate strategy for a situation. This Integrated Machine Learning System is used to model the learning behavior of Students. Given the teaching material the IMLS uses its ontology of Machine Learning Strategies to identify which Machine Learning strategy is applicable for each situation in the learning process. The IMLS also records the alternative learning strategies that may be suggested for the situation. These solution states are represented in Plausible Justification Trees .One of these plausible justification trees will result in the error made by the student. The wrong learning strategy used in that tree helps in identifying the learning error committed by the student.

[1]  Hanchuan Peng,et al.  Document Image Recognition Based on Template Matching of Component Block Projections , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Gheorghe Tecuci,et al.  Apprenticeship learning in imperfect domain theories , 1990 .

[3]  Rung Ching Chen,et al.  The recognition of form documents based on three types of line segments , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[4]  Ming Ye,et al.  Document image matching and annotation lifting , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[5]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[6]  Majid Ahmadi,et al.  Document registration using projective geometry , 1997, IEEE Trans. Image Process..

[7]  Etienne Wenger,et al.  Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge , 1987 .

[8]  William A. Barrett,et al.  Fourier–Mellin registration of line-delineated tabular document images , 2005, International Journal of Document Analysis and Recognition (IJDAR).

[9]  Ashwin Ram,et al.  Introspective reasoning using meta-explanations for multistrategy learning , 1995 .

[10]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[11]  Richard Zanibbi,et al.  A survey of table recognition , 2004, Document Analysis and Recognition.

[12]  Mikhail V. Matz A process model for high school algebra errors , 1982 .

[13]  Kurt VanLehn,et al.  Learning one Subprocedure per Lesson , 1987, Artif. Intell..