Latent Semantic Analysis for User Modeling

Authors:
Virginie Zampa;Benoît Lemaire
Affiliations:
L.S.E., University of Grenoble II, BP 47, 38040 Grenoble Cedex 9, France;L.S.E., University of Grenoble II, BP 47, 38040 Grenoble Cedex 9, France
Venue:
Journal of Intelligent Information Systems - Special issue: A survey of research questions for intelligent information systems in education
Year:
2002

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Abstract

Latent semantic analysis (LSA) is a tool for extracting semantic information from texts as well as a model of language learning based on the exposure to texts. We rely on LSA to represent the student model in a tutoring system. Domain examples and student productions are represented in a high-dimensional semantic space, automatically built from a statistical analysis of the co-occurrences of their lexemes. We also designed tutoring strategies to automatically detect lexeme misunderstandings and to select among the various examples of a domain the one which is best to expose the student to. Two systems are presented: the first one successively presents texts to be read by the student, selecting the next one according to the comprehension of the prior ones by the student. The second plays a board game (kalah) with the student in such a way that the next configuration of the board is supposed to be the most appropriate with respect to the semantic structure of the domain and the previous student's moves.