From Recording Linguistic Competence to Supporting Inferences about Language Acquisition in Context: Extending the Conceptualization of Student Models for Intelligent Computer-Assisted Language Learning

Student models for Intelligent Computer Assisted Language Learning (ICALL) have largely focused on the acquisition of grammatical structures. In this paper, we motivate a broader perspective of student models for ICALL that incorporates insights from current research on second language acquisition and language testing. We argue for a student model that includes a representation of the learner’s ability to use language in context and to perform tasks as well as for an explicit activity model that provides information on the language tasks and the inferences for the student model they support. The student model architecture we present is being developed as part of the TAGARELA system, an intelligent workbook supporting the instruction of Portuguese.