Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor

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 itemtypes. 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. 1 Carnegie Learning's Brid...

• Type: Conference
• Year: 2008
• Where: EDM
• Authors: Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koedinger
• Keywords: Cognitive Tutor, Data Mining, EDM 2008, Skill, Skill Model