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Quantifying Classroom Engagement: Statistical Comparison of EEG-Derived Attention and Meditation DataÂ

Author

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  • Nur Ashikin Muhammad Nasir

    (Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia)

  • Khairul Azha A Aziz

    (Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia)

  • Amar Faiz Zainal Abidin

    (Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia)

  • Syahrul Hisham Mohamad

    (Fakulti Teknologi dan Kejuruteraan Elektrik (FTKE), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia)

  • Rozi Rifin

    (Faculty of Electrical Engineering, Universiti Teknologi MARA Johor Branch, Pasir Gudang Campus, 81750 Masai, Johor, Malaysia)

Abstract

This study examines the statistical relationship between attention and meditation in undergraduate students during lectures using an EEG based monitoring system. Data were collected from ten students wearing a single node sensor MindLink headset where signals were acquired using HC-05 Bluetooth to an Arduino Uno and processed by a custom MATLAB GUI. Analytically, we adopted a within-subject framework, summarizing participant level averages and applying paired comparisons to test whether attention systematically exceeds meditation. Graphical diagnostics including grouped bar charts, distribution plots, identity line scatter, and delta profiles validated the inferential results, showing a consistent attention advantage across the cohort. These findings indicate that low-cost EEG sensor provides objective, quantifiable evidence of classroom engagement, complementing traditional observational and self report methods. The approach supports real-time tracking and offers a practical foundation for adaptive learning strategies driven by neuro signals.

Suggested Citation

  • Nur Ashikin Muhammad Nasir & Khairul Azha A Aziz & Amar Faiz Zainal Abidin & Syahrul Hisham Mohamad & Rozi Rifin, 2025. "Quantifying Classroom Engagement: Statistical Comparison of EEG-Derived Attention and Meditation DataÂ," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(9), pages 7080-7086, September.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-9:p:7080-7086
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