Literaturnachweis - Detailanzeige
| Autor/inn/en | Pavlik, Philip I., Jr.; Cen, Hao; Wu, Lili; Koedinger, Kenneth R. |
|---|---|
| Titel | Using Item-Type Performance Covariance to Improve the Skill Model of an Existing Tutor [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (1st, Montreal, Canada, Jun 20-21, 2008). |
| Quelle | (2008), (10 Seiten)
PDF als Volltext kostenfreie Datei |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Algebra; Intelligent Tutoring Systems; Statistical Analysis; Coding; Mathematics Skills; Correlation; Mathematics Instruction |
| Abstract | 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.] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2017/4/10 |
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