Abstract
Learner's motivation is one of the main aspects that need to be addressed for a successful learning process. Consequently, learner motivation assessment and measurement have attracted significant research interest in the e-learning area in general and game-based learning in particular. Traditional methodologies for learner motivation analysis rely on data collected through questionnaires. However, this approach does not fit well in the context of game-based learning, because an out-of-game questionnaire breaks the game user's flow and immersion. This paper presents a novel electroencephalography (EEG) sensor-based methodology that supports real-time non-disturbing automatic measurement and analysis of learner's motivation in game-based learning. An evaluation case study with participants playing an educational game was conducted in order to investigate the feasibility of the proposed sensor-based methodology and to compare the proposed methodology with the intrinsic motivation inventory-based questionnaire methodology. The results analysis has shown that the sensor-based methodology outperforms the traditional questionnaire-based methodology. The traditional questionnaire-based methodology is limited to analysing learner's motivation on short game-playing durations, while losing its feasibility when analysing learner's overall motivation over a long game-playing duration. Results have shown that learner's motivation changes in time during the game-play period and the learner's self-report of his/her motivation, assessed through the questionnaire-based methodology, tends to reflect only the last moments before the questionnaire is answered. Conversely, the proposed EEG sensor-based methodology is more suitable to analyse learner's motivation on both short and long game-playing durations (e.g. game tasks, game levels, etc.), with the additional benefit of not interrupting the game-play and not breaking the game flow and the learner's immersion with the game.
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