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Adaptive support for student learning in educational games

University of British Columbia

Educational games can be highly entertaining, but studies have shown that they are not always effective for learning. To enhance the effectiveness of educational games, we propose intelligent pedagogical agents that can provide individualized instruction that is integrated with the entertaining nature of these systems. We embedded one such animated pedagogical agent into the electronic educational game Prime Climb. To allow the agent to provide individualized help to students, we built a probabilistic student model that performs on-line assessment of student knowledge. To perform knowledge assessment, the student model accesses a student's game actions. By representing the probabilistic relations between these actions and the corresponding student's knowledge in a Bayesian Network, the student model assesses the evolution of this knowledge during game playing. We performed an empirical study to test the effectiveness of both the student model and the pedagogical agent. The results of the study strongly support the effectiveness of our approach.

Thesis/Dissertation

application/pdf

2009-10-06

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http://hdl.handle.net/2429/13648

Computer Science

University of British Columbia

UBCV

DSpace