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

Pınar Karabakla; Dilara Bakan Kalaycıoğlu; Nilüfer Kahraman

doi:10.21449/ijate.1743112

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

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.

Keywords

Ethical Statement

Gazi University Institutional Review Board, 91610558-604.01.02.

References

Anderson, J.R. (2010). Cognitive psychology and its implications (7th ed.). Worth Publishers.
Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425 438. https://doi.org/10.1007/s10648-010-9130-y
Bazanova, O.M., & Vernon, D. (2014). Interpreting EEG alpha activity. Neuroscience & Biobehavioral Reviews, 44, 94-110. https://doi.org/10.1016/j.neubiorev.2013.05.007
Başar, E. (2012). Brain oscillations in neuropsychiatric disease. Dialogues in Clinical Neuroscience, 14(4), 345-355. https://doi.org/10.31887/DCNS.2013.15.3/ebasar
Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., & Craven, P.L. (2007). EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviation, Space, and Environmental Medicine, 78(5), B231-B244.
Casson, A.J., Yates, D.C., Smith, S.J., Duncan, J.S., & Rodriguez-Villegas, E. (2010). Wearable electroencephalography. IEEE Engineering in Medicine and Biology Magazine, 29(3), 44-56. https://ieeexplore.ieee.org/document/5463026
Clark, R.C., & Mayer, R.E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th ed.). Wiley. https://doi.org/10.1002/9781119239086
Clayton, M.S., Yeung, N., & Cohen Kadosh, R. (2015). The roles of cortical oscillations in sustained attention. Trends in Cognitive Sciences, 19(4), 188 195. https://doi.org/10.1016/j.tics.2015.02.004

Cooley, J.W., & Tukey, J.W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297 301. https://doi.org/10.1090/S0025-5718-1965-0178586-1
Enriquez-Geppert, S., Huster, R.J., & Herrmann, C.S. (2017). EEG-Neurofeedback as a tool to modulate cognition and behavior: A review tutorial. Frontiers in Human Neuroscience, 11, Article 51. https://doi.org/10.3389/fnhum.2017.00051
Eskiköy, O., & Doğan, Ö. (Directors). (2008). On the way to school [Film]. Bulut Film; Perişan Film.
Fan, J., McCandliss, B.D., Sommer, T., Raz, A., & Posner, M.I. (2002). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14(3), 340 347. https://doi.org/10.1162/089892902317361886
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann Publishers.
Hawkins, D.M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1), 1-12. https://doi.org/10.1021/ci0342472
Ifenthaler, D., & Yau, J.Y.K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Research and Practice in Technology Enhanced Learning, 15(1), 1-12.
İki Dil Bir Bavul. (2025, 26 September). In Wikipedia. https://tr.wikipedia.org/wiki/%C4%B0ki_Dil_Bir_Bavul
iMotions. (2024). iMotions software [Computer software]. https://imotions.com
James, W. (1890). The principles of psychology. Henry Holt and Company.
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance. Brain Research Reviews, 29(2-3), 169-195.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on artificial intelligence (IJCAI) (pp. 1137 1143). Morgan Kaufmann Publishers. https://www.ijcai.org/Proceedings/95 2/Papers/016.pdf
Krigolson, O.E. (2017). Event-related brain potentials and the study of reward processing: Methodological considerations. International Journal of Psychophysiology, 132, 175-183.
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering, 4(2), R1-R13.
Luck, S.J. (2014). An introduction to the event-related potential technique (2nd ed.). MIT Press.
Mayer, R.E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
Michel, C.M., & Brunet, D. (2019). EEG source imaging: A practical review. Clinical Neurophysiology, 130(4), 524-544.
Moreno, R., & Mayer, R.E. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309-326. https://doi.org/10.1007/s10648-007-9047-2
Newson, J.J., & Thiagarajan, T.C. (2019). EEG frequency bands in psychiatric disorders: A review of resting state studies. Frontiers in Human Neuroscience, 12, Article 521. https://doi.org/10.3389/fnhum.2018.00521
Oostenveld, R., & Praamstra, P. (2001). The five percent electrode system for high‐resolution EEG and ERP measurements. Clinical Neurophysiology, 112(4), 713-719.
Posner, M.I., & Petersen, S.E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42. https://doi.org/10.1146/annurev.ne.13.030190.000325
Quinlan, J.R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann.
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press.
Tan, P.N., Steinbach, M., & Kumar, V. (2019). Introduction to data mining (2nd ed.). Pearson.
Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1-11.
Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7, Article 91. https://doi.org/10.1186/1471-2105-7-91

Details

Primary Language

English

Subjects

Measurement and Evaluation in Education (Other)

Journal Section

Research Article

Authors

Pınar Karabakla ^*
0009-0007-9202-6142
Türkiye

Dilara Bakan Kalaycıoğlu
0000-0003-1447-6918
Türkiye

Nilüfer Kahraman
0000-0003-2523-0155
Türkiye

Early Pub Date

April 23, 2026

Publication Date

-

Submission Date

July 19, 2025

Acceptance Date

February 25, 2026

Published in Issue

Year 2026 Number: Advanced Online Publication

DOI

https://doi.org/10.21449/ijate.1743112

IZ

https://izlik.org/JA86RB99PN

Cite

RIS / Bibtex

APA

Karabakla, P., Bakan Kalaycıoğlu, D., & Kahraman, N. (2026). EEG-based evaluation of student attention in multimedia learning environments: A pilot study. International Journal of Assessment Tools in Education, Advanced Online Publication. https://doi.org/10.21449/ijate.1743112

AMA

1.Karabakla P, Bakan Kalaycıoğlu D, Kahraman N. EEG-based evaluation of student attention in multimedia learning environments: A pilot study. Int. J. Assess. Tools Educ. 2026;(Advanced Online Publication). doi:10.21449/ijate.1743112

Chicago

Karabakla, Pınar, Dilara Bakan Kalaycıoğlu, and Nilüfer Kahraman. 2026. “EEG-Based Evaluation of Student Attention in Multimedia Learning Environments: A Pilot Study”. International Journal of Assessment Tools in Education, no. Advanced Online Publication. https://doi.org/10.21449/ijate.1743112.

EndNote

Karabakla P, Bakan Kalaycıoğlu D, Kahraman N (April 1, 2026) EEG-based evaluation of student attention in multimedia learning environments: A pilot study. International Journal of Assessment Tools in Education Advanced Online Publication

IEEE

[1]P. Karabakla, D. Bakan Kalaycıoğlu, and N. Kahraman, “EEG-based evaluation of student attention in multimedia learning environments: A pilot study”, Int. J. Assess. Tools Educ., no. Advanced Online Publication, Apr. 2026, doi: 10.21449/ijate.1743112.

ISNAD

Karabakla, Pınar - Bakan Kalaycıoğlu, Dilara - Kahraman, Nilüfer. “EEG-Based Evaluation of Student Attention in Multimedia Learning Environments: A Pilot Study”. International Journal of Assessment Tools in Education. Advanced Online Publication (April 1, 2026). https://doi.org/10.21449/ijate.1743112.

JAMA

1.Karabakla P, Bakan Kalaycıoğlu D, Kahraman N. EEG-based evaluation of student attention in multimedia learning environments: A pilot study. Int. J. Assess. Tools Educ. 2026. doi:10.21449/ijate.1743112.

MLA

Karabakla, Pınar, et al. “EEG-Based Evaluation of Student Attention in Multimedia Learning Environments: A Pilot Study”. International Journal of Assessment Tools in Education, no. Advanced Online Publication, Apr. 2026, doi:10.21449/ijate.1743112.

Vancouver

1.Pınar Karabakla, Dilara Bakan Kalaycıoğlu, Nilüfer Kahraman. EEG-based evaluation of student attention in multimedia learning environments: A pilot study. Int. J. Assess. Tools Educ. 2026 Apr. 1;(Advanced Online Publication). doi:10.21449/ijate.1743112