Abstract
Flow and Immersion are states of deep focus and thorough concentration on an activity, in which the subjective perception of performance reaches an optimum and intrinsic motivation peaks. High intrinsic motivation and deep focus does not only influence learning effects positively, deriving or enriching user models with raw and processed physiological data might also prove invaluable for successful adaptation processes that may be used to further improve learning outcome. So far, there is no reliable method to underpin states of deep focus with physiological characteristics, which would allow detecting such states objectively. Both Flow and Immersion are therefore classically measured using questionnaires. Given that the subjects are not answering the questionnaires during the activity, thus potentially breaking chances to reach states of Flow and Immersion, this method is both highly subjective and delayed - at least the latter somewhat impacting on the accuracy of the questionnaires results. To address these shortcomings, the design of a study to measure deep focus states through finding correlations between questionnaire answers and physiological sensor data (galvanic skin response, electrocardiography, eye tracking) is briefly referenced. The results of the study are discussed, motivating why the Flow model, as is, needs to be revised to allow a more fine grained measurement approach.
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Kannegieser, E., Atorf, D., Herold, J. (2021). Measuring Flow, Immersion and Arousal/Valence for Application in Adaptive Learning Systems. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Adaptation Strategies and Methods. HCII 2021. Lecture Notes in Computer Science(), vol 12793. Springer, Cham. https://doi.org/10.1007/978-3-030-77873-6_5
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