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Journals / International Journal of Assessment Tools in Education / 2026 / Volume: 13 Issue: 3

EEG-based evaluation of student attention in multimedia learning environments: A pilot study

Pınar Karabakla(GAZİ ÜNİVERSİTESİ)0009-0007-9202-6142
Dilara Bakan Kalaycıoğlu(GAZI UNIVERSITY, GAZİ FACULTY OF EDUCATION)0000-0003-1447-6918
Nilüfer Kahraman(Gazi Üniversitesi)0000-0003-2523-0155
Pages
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Publication Date
2026-09-02
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Bayesian Classification achieved the highest overall performance for both questions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This pilot study investigates the feasibility of using electroencephalogram (EEG) signals to assess students’ attention in a multimedia learning environment and to examine whether EEG-derived features can differentiate correct and incorrect responses to comprehension questions.
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Key Findings & Data:EEG data were collected from 25 university students while they watched a short educational video and answered two follow-up questions.
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Conclusion & Impact:Bayesian Classification achieved the highest overall performance for both questions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:EEG · Attention · Multimedia learning

Abstract

This pilot study investigates the feasibility of using electroencephalogram (EEG) signals to assess students’ attention in a multimedia learning environment and to examine whether EEG-derived features can differentiate correct and incorrect responses to comprehension questions. EEG data were collected from 25 university students while they watched a short educational video and answered two follow-up questions. Power spectral density values of Delta, Theta, Alpha, Beta, and Gamma frequency bands were extracted and used as input features for five classification algorithms: Bayesian Classification, Logistic Regression, C5.0, CHAID, and Artificial Neural Networks. Model performance was evaluated using accuracy, sensitivity, specificity, and F1-score. Bayesian Classification achieved the highest overall performance for both questions. Across models, Beta and Gamma band activities emerged as the most informative features for distinguishing correct from incorrect responses. These findings indicate that EEG-based measures can provide objective indicators of attention-related cognitive engagement in multimedia learning contexts. The results demonstrate the potential of combining EEG and machine learning for attention assessment and support the feasibility of further large-scale investigations.

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Cite This Article

Karabakla, P., Kalaycıoğlu, D. B., Kahraman, N. (2026). EEG-based evaluation of student attention in multimedia learning environments: A pilot study. International Journal of Assessment Tools in Education, Volume: 13 Issue: 3. https://doi.org/10.21449/ijate.1743112

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