Abstract
Visual Representation of Skill Level Mastery Profiles in 2-Dimensional Space Track: Education Author(s): Charisse Gulosino, Enis Dogan Rule Space Model (RSM) is a testing model that aims to infer a given test-taker's mastery patterns of cognitive skills that underlie a test using examinee's item response pattern and the hypothesized relationship between items and skills, described by a Q matrix. To classify examinees into Knowledge States, RSM compares ideal response patterns to observed ones by projecting both onto 2-dimensional space and calculating the distance between them. Data came from 5,000 examinees who took the 2004 University Entrance Examination of Turkey. All examinees were classified successfullyinto Knowledge States using a list of 15 skills. Here we suggest that distribution of skill mastery probabilities and Knowledge States can be displayed in 2-dimensional RSM using the ArcGIS Spatial Analyst extension for distance modeling. This will enable us to see visualize thedistance between Knowledge States at given regions of the mastery probability distribution, hence provide a richer understanding of the relationship between skills. Charisse Gulosino Teachers College Columbia University 42-15, 76th Street Elmhurst, New York , NY 11373 US Phone: 646-251-1834 E-mail: cag2022@columbia.edu Enis Dogan American Institute for Research 1000 Thomas Jefferson Street Washington, D.C. 20007 US Phone: 6462690035 E-mail: enisdogan@hotmail.com |
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