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ERIC Number: ED506306
Record Type: Non-Journal
Publication Date: 2009
Pages: 10
Abstractor: As Provided
Reference Count: 12
Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models
Pavlik, Philip I. Jr.; Cen, Hao; Koedinger, Kenneth R.
Online Submission, Paper presented at The International Conference on Educational Data Mining (2nd, Cordoba, Spain, 2009)
This paper describes a novel method to create a quantitative model of an educational content domain of related practice item-types using learning curves. By using a pairwise test to search for the relationships between learning curves for these item-types, we show how the test results in a set of pairwise transfer relationships that can be expressed in a Q-matrix domain model. Creating these Q-matrices for various test criteria we show that the new domain model results in consistently better learning curve fits as shown by cross-validation. Further, the Q-matrices produced can be used by educators or curriculum designers to gain a richer, more integrated perspective on concepts in the domain. The model may also have implications for tracing student knowledge more effectively to sequence practice in tutoring/training software. (Contains 2 figures.) [This research was funded by the U.S. Department of Education, Carnegie Learning Inc., the Pittsburgh Science of Learning Center and DataShop team, and Ronald Zdrojkowski.]
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A