Title

Predicting performance behaviors during question generation in a game-like intelligent tutoring system

Abstract

The present research investigates learning constructs predicting performance behaviors during question generation in a serious game known as Operation ARA. In a between-subjects design, undergraduate students (N=66) completed the three teaching modules of the game, teaching the basic factual information, application of knowledge, and finally question generation about scientific research cases. Results suggest that constructs such as time-on-task, discrimination, and generation along with type of instruction (factual vs. applied) impact student behaviors during question generation.

Publication Title

Proceedings of International Conference of the Learning Sciences, ICLS

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