Key result
HRV and cognitive absorption model predicts student physiological flow during STEAM activities with ~94% accuracy.
Why the study?
The study aimed to advance the understanding of motivation in terms of flow in groups from a physiological perspective.
Observational (n=28)
No
Effect estimate: R2 0.941
p-value: p=7.7x10-77
Heart rate variability monitored via wearable devices can be used to mathematically model and predict group flow and cognitive absorption in educational settings.
This paper advances in the understanding of motivation in terms of flow in groups from a physiological perspective. We use wearable devices to monitor the heart rate variation during a set of sessions of face-to-face STEAM project-based learning. By using Action Research with mixed-methods design, we observed a set of 28 students in real-world settings during 18 classes and used both customized and commercial tools to analyze data retrieved. Based on the cognitive absorption and motivation obtained from EduFlow-scale-based physiological data, we propose mathematical models to predict the Flow that a group will experience in a teaching–learning session. Our preliminary results may challenge the central axiom of Flow Theory, while clarifies the balance hypothesis.
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Rosas et al. (2025) conducted an observational in Healthy primary education students (n=28). STEAM project-based learning was evaluated on Mathematical model predicting group flow based on HRV physiological response (R2 0.941, p=7.7x10-77). A robust mathematical model based on heart rate variability and cognitive absorption accurately predicted physiological flow in primary school groups during STEAM activities (R2 = 0.941).
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