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ERIC Number: ED504854
Record Type: Non-Journal
Publication Date: 2008
Pages: 10
Abstractor: As Provided
Reference Count: 10
Using Item-Type Performance Covariance to Improve the Skill Model of an Existing Tutor
Pavlik, Philip I., Jr.; Cen, Hao; Wu, Lili; Koedinger, Kenneth R.
Online Submission, Paper presented at the International Conference on Educational Data Mining (1st, Montreal, Canada, Jun 20-21, 2008)
Using data from an existing pre-algebra computer-based tutor, we analyzed the covariance of item-types with the goal of describing a more effective way to assign skill labels to item-types. Analyzing covariance is important because it allows us to place the skills in a related network in which we can identify the role each skill plays in learning the overall domain. This placement allows more effective and automatic assignment of skills to item-types. To analyze covariance we used POKS (partial order knowledge structures) to analyze item-type outcome relationships and Pearson correlation to capture item-type duration relationships. Hierarchical agglomerative clustering of these item-types was also performed using both outcome and duration covariance patterns. These analyses allowed us to propose improved skill labeling that removes irrelevant item-types, clusters related types, and clarifies the optimal temporal ordering of these clusters during practice. (Contains 1 figure and 1 table.) [This paper was published in: Proceedings of the International Conference on Educational Data Mining (1st, Montreal, Canada, June 20-21, 2008). R. S. Baker and J. E. Beck, Editors. pp. 77-86.]
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305B070487