Atomic Dynamic Bayesian Networks for a Responsive Student Model

Authors:
Fang Wei;Glenn D. Blank
Affiliations:
Computer Science & Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, PA 18015, USA, faw2@lehigh.edu, lennblank@gmail.com;Computer Science & Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, PA 18015, USA, faw2@lehigh.edu, lennblank@gmail.com
Venue:
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Year:
2007

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Abstract

Atomic Dynamic Bayesian Networks (ADBNs) combine several valuable features in student models: students' performance history, prerequisite relationships, concept to solution step relationships, and student real time responsiveness. Recent work addresses some of these features but has not combined them. Such a combination is needed in an ITS that helps students learn, step by step, in a complex domain, such as object-oriented design. We evaluated ADBN-based student models 49 human students, investigating their behavior for different types of students and different slip and guess values. Holding slip and guess to equal, small values, ADBNs are able to produce accurate diagnostic rates for knowledge states over each student's learning history.