This study investigated motivational strategies and the assessment of learners' motivation during serious gameplay. Identifying and intelligently assessing the effects that these strategies may have on learners are particularly relevant for educational computer-based systems. We proposed, therefore, the use of physiological sensors, namely, heart rate, skin conductance, and electroencephalogram (EEG), as well as a theoretical model of motivation (Keller's ARCS model) to evaluate six motivational strategies selected from a serious game called Food-Force. Results from nonparametric tests and logistic regressions supported the hypothesis that physiological patterns and their evolution are suitable tools to directly and reliably assess the effects of selected strategies on learners' motivation. They showed that specific EEG "attention ratio" was a significant predictor of learners' motivation and could relevantly evaluate motivational strategies, especially those associated with the Attention and Confidence categories of the ARCS model of motivation. Serious games and intelligent systems can greatly benefit from using these results to enhance and adapt their interventions.
This study investigated players' motivation during serious game play. It is based on a theoretical model of motivation. Statistical analysis showed a significant increase of motivation during the game. This study tried to dissect predictors of Players' Motivational States. Multiple linear regression showed statistical significance of specific electrophysiological data. The theta wave in the frontal regions and motivation were positively correlated. High-beta wave in the left-center region was also a significant predictor for high level of motivation. Skin conductance was also a significant predictor for motivation. However, we could not find a significant correlation between players' motivation and their heart rate responses.
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