A theory of student modelling in instructional expert systems

In order to provide effective instruction (i.e. present the material at the correct level, use the most appropriate teaching strategy, select appropriate remedial action, etc.), an Instructional Expert System must maintain an accurate "student model". We propose a theory (based on the concept of "rough classification") by which Instructional Expert Systems may acquire a deep understanding of a student. By using the indiscernibility relation, the expert's knowledge and the student's knowledge of a particular domain can be expressed in terms of equivalence classes which partition the domain. By comparing both partitions, the computer tutor may ascertain what the student knows, the student's misconceptions, and what can and can't the student learn.