Exploiting The Student Model To Emphasize Language Pedagogy In Natural Language Processing

One of the typical problems of Natural Language Processing (NLP) is the explosive property of the parser and this is aggravated in an Intelligent Language Tutoring System (ILTS) because the grammar is unconstrained and admits even more analyses. NLP applications frequently incorporate techniques for selecting a preferred parse. Computational criteria, however, are insufficient for a pedagogic system because the parse chosen will possibly result in misleading feedback for the learner. Preferably, the analysis emphasizes language teaching pedagogy by selecting the sentence interpretation a student most likely intended. In the system described in this paper, several modules are responsible for selecting the appropriate analysis and these are informed by the Student Model. Aspects in the Student Model play an important pedagogic role in determining the desired sentence interpretation, handling multiple errors, and deciding on the level of interaction with the student.