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A model and adaptive support for learning in an educational game

University of British Columbia

Educational games are highly motivating, however, there is little evidence that they can trigger learning unless game-play is supported by additional activities. We aim to support students during game play via an intelligent pedagogical agent that intervenes to offer hints and suggestions when the student is lacking domain knowledge, but does not interfere otherwise, so as to maintain the engagement that computer games are known to bring about. Such an agent must be informed by an accurate model of student learning. In this thesis we describe research on data-drive refinement and evaluation of a probabilistic model of student learning for an educational game on number factoriza- tion, Prime Climb. An initial version of the model was designed based on teachers' advice and subjective parameter settings. We illustrate data-driven improvements to the model, and we report significant improvements on its accuracy. This model is used by an intelligent pedagogical agent for Prime Climb. We present results from an ablation study in which students played with a version of the game which employed either a pedagogical agent acting on the original model, a pedagogical agent acting on the new model, or no pedagogical agent at all. Learning gains and students' subjective assessments of the agent are discussed.

Text

2010-01-06

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http://hdl.handle.net/2429/17581

Computer Science

University of British Columbia

UBCV

DSpace