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Predicting transfer from a game-based learning environment
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DOI：10.1016/j.compedu.2019.103780.png)
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Abstract
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This study examined the predictive impact of variables from self-regulated learning models on transfer following multiple gameplay sessions in a classroom game-based learning environment (GBLE). The game focused on science curriculum including map models, map navigation, and landforms. Fifth-grade students (N = 594) interacted with CRYSTAL ISLAND - UNCHARTED DISCOVERY during six 50-min sessions over four weeks integrated with classroom instruction. The transfer activity required students to create an island on grid paper that included at least seven different landforms, a map navigation activity created for peers, and an attached map scale model. Results of multiple regression analyses revealed that when other variables were accounted for, none of the measured motivational variables including interest for the game, performance-approach goal orientation, mastery-approach goal orientation, or self-efficacy for science were significant positive predictors of transfer. Interestingly, video game self-efficacy was a significant negative predictor of transfer performance. Prior knowledge and change in science knowledge after gameplay were also significant predictors of transfer. Girls performed as well as boys on the transfer task despite completing less game quests and had significantly lower posttest content scores. Interest was a consistent predictor of performance for girls but not boys for in-game performance, posttest content scores, and transfer scores. Regression models were systematically less able to predict performance moving from content knowledge, to in-game performance, to transfer. Implications for the design of GBLEs to facilitate transfer are discussed.
Keywords:
Game-based learning
Transfer
Science
Gender
Motivation
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