Latent semantic analysis for user modeling

Latent semantic analysis (LSA) is a tool for extracting semantic informatio n 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 syste m. Domain examples and student productions are represented in a high-dimens ional semantic space, automatically built from a statistical analysis of th e co-occurrences of their lexemes. We also designed tutoring strategies to automatically detect lexeme misunderstandings and to select among the vario us 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 rea d 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 t he student in such a way that the next configuration of the board is suppos ed to be the most appropriate with respect to the semantic structure of the domain and the previous student's moves.