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Student Modeling for Language Tutors

Student modeling is of great importance in intelligent tutoring and intelligent educational assessment applications. However, student modeling for computer-assisted language learning (CALL) applications differs from classic student modeling in several key ways, including the lack of observable intermediate steps (behavioral or cognitive) involved in successful performance. This workshop will focus on student modeling for intelligent CALL applications, addressing such domains as reading decoding and reading and spoken language comprehension. Domains of interest include both primary (L1) and second language (L2) learning. Hence, the workshop will address questions related to student modeling for CALL, including what types of knowledge ought such a model contain, with what design rationale, and how might information about the user's knowledge be obtained and/or inferred in a CALL context?