%0 Conference Paper %1 Webb88a %A Webb, G. I. %B Computers in Learning in Tertiary education: Proceedings of the Sixth Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE-88) %D 1988 %E Fielden, K. %E Hicks, F. %E Scott, N. %K Based Computer Feature Learning Modeling, %P 502-514 %T Cognitive Diagnosis Using Student Attributions %X 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.

@inproceedings{Webb88a, abstract = {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.}, added-at = {2016-03-20T05:42:04.000+0100}, audit-trail = {*}, author = {Webb, G. I.}, biburl = {https://www.bibsonomy.org/bibtex/2d6639b6d89e610204afe138f5d2e2483/giwebb}, booktitle = {Computers in Learning in Tertiary education: Proceedings of the Sixth Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE-88)}, editor = {Fielden, K. and Hicks, F. and Scott, N.}, interhash = {48167cdc1f09ab714f22d42aa0249698}, intrahash = {d6639b6d89e610204afe138f5d2e2483}, keywords = {Based Computer Feature Learning Modeling,}, location = {Canberra, Australia}, pages = {502-514}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Cognitive Diagnosis Using Student Attributions}, year = 1988 }