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. 1. Introduction It is widely acknowledged that learners’ psychological and cognitive states have an important role in intelligent systems and serious games (SGs). For instance, engagement and motivation or disaffection and boredom obviously affect learners’ wills and skills in acquiring new knowledge [1]. SGs cannot, therefore, ignore these states and should take them into account during learning process. One important learners’ state is motivation which plays a crucial role in both the learners’ performance and the use of intelligent systems over time [2]. Motivation is generally defined as that which explains the direction and magnitude of behaviour, or in other words, it explains what goals people choose to pursue and how they pursue them [3]. It is considered as a natural part of any learning process. Several researches have showed that motivated learners are more likely to be more engaged, to undertake challenging activities, and to exhibit enhanced performance and outcomes [4, 5]. Therefore, it is of particular relevance to study motivation and its role in improving learners’ performance during gameplay. Learners’ interactions with Intelligent Tutoring Systems (ITSs) and especially SGs have always been considered to be intrinsically motivating. One possible explanation is the fact that ITSs generally use pictures, sounds, videos, and so forth, that are considered, crudely, as
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