Mining Diagnostic Assessment Data for Concept Similarity

This paper introduces a method for mining multiple-choice assessment data for similarity of the concepts represented by the multiple choice responses. The resulting similarity matrix can be used to visualize the distance between concepts in a lower-dimensional space. This gives an instructor a visualization of the relative difficulty of concepts among the students in the class. It may also be used to cluster concepts, to understand unknown responses in the context of previously identified concepts.

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Related Organizations

University of Washington
United States
University of Mary
United States

Keywords

concept similarity, student modeling, diagnostic assessment, misconceptions, individual differences, visualization

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