Make Silence Speak for Itself: a multi-modal learning analytic approach with neurophysiological data

Mingxuan Gao; Jingjing Chen; Yun Long; Xiaomeng Xu; Yu Zhang

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arXiv · 2507.21063

Make Silence Speak for Itself: a multi-modal learning analytic approach with neurophysiological data

Mingxuan Gao·Jingjing Chen·Yun Long·Xiaomeng Xu·Yu Zhang

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Abstract

Background: Silence is a common phenomenon in classrooms, yet its implicit nature limits a clear understanding of students' underlying learning statuses. Aim: This study proposed a nuanced framework to classify classroom silence based on class events and student status, and examined neurophysiological markers to reveal similarities and differences in silent states across achievement groups. Sample: The study involved 54 middle school students during 34 math lessons, with simultaneous recordings of electroencephalogram (EEG), electrodermal activity (EDA), and heart rate signals, alongside video coding of classroom behaviors. Results: We found that high-achieving students showed no significant difference in mean EDA features between strategic silence (i.e., students choose silence deliberately) and active speaking during open questioning but exhibited higher EEG high-frequency relative power spectral density (RPSD) during strategic silence. In structural silence (i.e., students maintain silence following an external command) during directed questioning, they demonstrated significantly higher heart rates while listening to lectures compared to group activities, indicating heightened engagement. Both high- and medium-achieving students displayed elevated heart rates and EDA tonic components in structural silence during questioning compared to teaching. Furthermore, high-achieving students exhibited lower high-frequency RPSD during structural silence than strategic silence, a pattern not observed in other groups, highlighting group heterogeneity. Conclusions: The findings contribute to validating the complexity of silence, challenge its traditional association with passivity, and offer a novel classification framework along with preliminary empirical evidence to deepen the understanding of silent learning behaviors in classroom contexts.

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Mingxuan Gao, Jingjing Chen, Yun Long, Xiaomeng Xu, Yu Zhang. 2025-05-23. Make Silence Speak for Itself: a multi-modal learning analytic approach with neurophysiological data. https://arxiv.org/abs/2507.21063

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