Cognitive Diagnosis Using Student Attributions

This paper details an approach to cognitive diagnosis that enables the inference of detailed models of a student’s conceptualisation of a domain. This model is constructed be examining the attributes of the problems that the student has tackled and the student’s performance while tackling those problems. A feature network is used to represent educationally relevant domain knowledge. • This approach has low implementation and operational overheads. • It provides a detailed model of the student’s conceptualisation of the subject domain in terms of elements of knowledge from that domain; • Student models are not restricted to overlays of predefined correct and/or incorrect knowledge. • It does not require that the instructional designer anticipate the possible forms of error that may occur. • It is robust in the face of partial evaluation of student performance. • It is also robust in the face of the instructional designer’s failure to incorporate relevant aspects of the subject domain in the knowledge-base. • The student models can be executed. • It supports accurate diagnosis of multiple viewpoints of the domain even when those viewpoints are not anticipated by the instructional designer. • It can support multiple teaching styles in the one lesson.