| Abstract | Flow states represent optimal conditions for learning, however, measuring flow in immersive learning environments (ILEs) is challenging. Self-report measures interrupt the experience being assessed, while behavioural observa-tion cannot access internal states. This paper introduces TriFlow-ILE, a trian-gulated framework that integrates lightweight electroencephalography (EEG), behavioural observation, and brief micro self-reports to evaluate flow during authentic creative activity. In a within-subject pilot (n = 4), each participant completed two 30-minute mask-sculpting sessions—clay and VR. Participants wore 8-channel EEG cap while creating masks in both media, with continuous neurophysiological recording, dual-angle video capture, and post-session as-sessments on flow, immersion and cognitive workload. This pilot implementa-tion of the framework evaluates its feasibility, identifying strengths, limita-tions, and refinements needed for larger scale implementation. Main findings include successful EEG data capture during active VR use, identification of individual baseline calibration requirements, and validation of the three-source protocol for flow classification. The framework offers ILE researchers a repli-cable method for objective engagement monitoring without experience disrup-tion. The discussion includes implications for adapting learning systems, tech-nical challenges in combining EEG with VR hardware, and methodological lessons learned that inform the ongoing main study. |
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