Article Open Access

EEG-Based Focus Analysis to Evaluate the Effectiveness of Active Learning Approaches

I Putu Agus Eka Darma Udayana, Made Sudarma, I Ketut Gede Darma Putra, I Made Sukarsa, Minho Jo

Abstract


Electroencephalography (EEG) has emerged as a non-invasive and objective technique for monitoring brain activity in real time, widely applied to measure cognitive states such as concentration and alertness. Its ability to capture brain responses during learning processes makes EEG a promising tool to evaluate student engagement more accurately than conventional methods. This study investigates the effectiveness of two active learning methods, Project-Based Learning (PjBL) and Problem-Based Learning (PBL), in the context of English tutoring for elementary students using EEG signals as a cognitive indicator. A total of 20 students aged 8–12 years from ThinkerBee Learning Centre Bali participated in the study. EEG data were recorded using the Muse 2 Headband while students completed test-based tasks designed for each learning method. The EEG signals were preprocessed using bandpass filtering, Continuous Wavelet Transform (CWT), and frequency band decomposition. Concentration scores were then calculated using two approaches: a heuristic method based on the Beta/(Theta + Alpha) ratio and a Long Short-Term Memory (LSTM) model. The heuristic method produced average scores of 0.3991 (PjBL) and 0.3822 (PBL), with a 4.42% difference, while the LSTM model showed a more substantial difference, with scores of 0.5454 (PjBL) and 0.4265 (PBL). A Spearman correlation test between EEG-derived scores and students’ academic results yielded a perfect correlation value of 1.0000, indicating a strong relationship between cognitive engagement and learning outcomes. These results demonstrate the potential of EEG as a reliable tool for objectively assessing learning effectiveness in primary education contexts.


Keywords


Electroencephalography, Concentration, Learning Method, Long Short-Term Memory, Focus Analysis

References


A. Chaddad, Y. Wu, R. Kateb, and A. Bouridane, “Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques,” Sensors, vol. 23, no. 14, p. 6434, 2023, doi: doi.org/10.3390/s23146434.

L. Xu, X. Xing, J. Chang, and P. Lin, “A Multi-Domain Coupled Spatio-temporal Feature Interaction Model for EEG Emotion Recognition,” IEEE Trans. Instrum. Meas., 2025, doi: 10.1109/TIM.2025.3571107.

B. Zali-Vargahan, A. Charmin, H. Kalbkhani, and S. Barghandan, “Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals,” Biomed. Signal Process. Control, vol. 85, p. 104806, 2023, doi: https://doi.org/10.1016/j.bspc.2023.104806.

M. Grobbelaar et al., “A survey on denoising techniques of electroencephalogram signals using wavelet transform,” Signals, vol. 3, no. 3, pp. 577–586, 2022, doi: 10.3390/signals3030035.

S. N. S. S. Daud and R. Sudirman, “Wavelet based filters for artifact elimination in electroencephalography signal: A review,” Ann. Biomed. Eng., vol. 50, no. 10, pp. 1271–1291, 2022, doi: 10.1007/s10439-022-03053-5.

O. Almanza-Conejo, D. L. Almanza-Ojeda, J. L. Contreras-Hernandez, and M. A. Ibarra-Manzano, “Emotion recognition in EEG signals using the continuous wavelet transform and CNNs,” Neural Comput. Appl., vol. 35, no. 2, pp. 1409–1422, 2023, doi: 10.1007/s00521-022-07843-9.

Z. Huang and M. Wang, “A review of electroencephalogram signal processing methods for brain-controlled robots,” Cogn. Robot., vol. 1, pp. 111–124, 2021, doi: https://doi.org/10.1016/j.cogr.2021.07.001.

L. F. Morán Mirabal, L. M. Martínez Álvarez, and J. A. Ruiz Ramirez, “Muse 2 headband specifications (neuronal tracking).” Institute for the Future of Education| Living Lab & Data Hub, pp. 1–3, 2022.

Z.-T. Liu, S.-J. Hu, J. She, Z. Yang, and X. Xu, “Electroencephalogram emotion recognition using combined features in variational mode decomposition domain,” IEEE Trans. Cogn. Dev. Syst., vol. 15, no. 3, pp. 1595–1604, 2023, doi: 10.1109/TCDS.2022.3233858.

J. Krajcik et al., “Assessing the effect of project-based learning on science learning in elementary schools,” Am. Educ. Res. J., vol. 60, no. 1, pp. 70–102, 2023, doi: 10.3102/000283122211292.

R. Amini, B. Setiawan, Y. Fitria, and Y. Ningsih, “The difference of students learning outcomes using the project-based learning and problem-based learning model in terms of self-efficacy,” in Journal of Physics: Conference Series, 2019, vol. 1387, no. 1, p. 12082. doi: 10.1088/1742-6596/1387/1/012082.

R. Simbolon and H. D. Koeswanti, “Comparison of Pbl (Project Based Learning) models with Pbl (Problem Based Learning) models to determine student learning outcomes and motivation,” Int. J. Elem. Educ., vol. 4, no. 4, pp. 519–529, 2020, doi: 10.23887/ijee.v4i4.30087.

T. Setiawan, J. M. Sumilat, N. M. Paruntu, and N. N. Monigir, “Analisis Penerapan model Pembelajaran project based learning Dan problem based learning pada Peserta Didik Sekolah Dasar,” J. Basicedu, vol. 6, no. 6, pp. 9736–9744, 2022, doi: doi.org/10.31004/basicedu.v6i6.4161.

T. Adicondro and I. Anugraheni, “Pengaruh Problem Based Learning (Pbl) Dan Project Based Learning (PJBL) Terhadap Hasil Belajar Ipa Siswa Sekolah Dasar,” J. Ilm. Wahana Pendidik., vol. 8, no. 14, pp. 452–461, 2022, doi: 10.5281/zenodo.7016068.

P. Sarma and S. Barma, “Review on stimuli presentation for affect analysis based on EEG,” IEEE Access, vol. 8, pp. 51991–52009, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2980893.

G. A. V. M. Giri and M. L. Radhitya, “Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 18, no. 3, doi: https://doi.org/10.22146/ijccs.96665.

D. L. Sherman and N. V Thakor, “Eeg signal processing: Theory and applications,” in Neural Engineering, Springer, 2020, pp. 97–129. doi: 10.1007/978-3-030-43395-6_3.

M. Felja, A. Bencheqroune, M. Karim, and G. Bennis, “Removing artifacts from EEG signal using wavelet transform and conventional filters,” WSEAS Trans. Inf. Sci. Appl., vol. 17, pp. 177–183, 2020, doi: 10.37394/23209.2020.17.22.

M. T. Sadiq et al., “Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform,” IEEE access, vol. 7, pp. 127678–127692, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2939623.

Z. Khakim and S. Kusrohmaniah, “Dasar-Dasar Electroencephalography (EEG) bagi Riset Psikologi,” Bul. Psikol., vol. 29, no. 1, pp. 92–115, 2021, doi: 10.22146/buletinpsikologi.52328.

C. Zhang, A. A. Mousavi, S. F. Masri, G. Gholipour, K. Yan, and X. Li, “Vibration feature extraction using signal processing techniques for structural health monitoring: A review,” Mech. Syst. Signal Process., vol. 177, p. 109175, 2022, doi: https://doi.org/10.1016/j.ymssp.2022.109175.

M. Priyadarshani, P. Kumar, K. S. Babulal, D. S. Rajput, and H. Patel, “Human brain waves study using EEG and deep learning for emotion recognition,” IEEE Access, vol. 12, no. 1, pp. 101842–101850, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3427822.

A. D. Wibawa, D. P. Wulandari, P. S. Rahayu, and W. R. Islamiyah, “Statistical analysis of subject-specific EEG data during stroke rehabilitation monitoring,” in 2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), 2020, pp. 168–172. doi: https://doi.org/10.1109/EECCIS49483.2020.9263462.

S. Poorani and P. Balasubramanie, “Seizure detection based on eeg signals using asymmetrical back propagation neural network method,” Circuits, Syst. Signal Process., vol. 40, no. 9, pp. 4614–4632, 2021, doi: 10.1007/s00034-021-01686-w.

E. Gani, A. Rio, M. Nugraha, and F. Haryanto, “The Effect of Myopia on Brain Signals: Insights from EEG Studies,” J. Penelit. Fis. dan Apl., vol. 14, no. 1, pp. 19–32, 2024, doi: https://doi.org/10.26740/jpfa.v14n1.p19-32.

Renuka Nyayadhish et al, “Fake News Detection in Model Integral: A Hybrid CNN-BiLSTM Model,” Int. J. Eng. Sci. Inf. Technol., vol. 5, no. 3, pp. 337–343, 2025, doi: 10.52088/ijesty.v5i3.1058.

F. K. Supriyono Supriyono, Aji Prasetya Wibawa, Suyono Suyono, “Enhancing Teks Summarization of Humorous Texts with Attention-Augmented LSTM and Discourse-Aware Decoding,” Int. J. Eng. Sci. Inf. Technol., vol. 5, no. 3, pp. 156–168, 2025, doi: 10.52088/ijesty.v5i3.932.

M. S. Dehnavi, V. S. Dehnavi, and M. Shafiee, “Classification of mental states of human concentration based on EEG signal,” in 2021 12th International Conference on Information and Knowledge Technology (IKT), 2021, pp. 78–82. doi: https://doi.org/10.1109/IKT54664.2021.9685731.

F. Santosa, E. Oktafanda, H. Setiawan, and A. Latif, “Advanced Long Short-Term Memory (LSTM) Models for Forecasting Indonesian Stock Prices,” J. Galaksi, vol. 1, no. 3, pp. 198–208, 2024, doi: https://doi.org/10.70103/galaksi.v1i3.42.

J. Zhao, X. Mao, and L. Chen, “Speech emotion recognition using deep 1D & 2D CNN LSTM networks,” Biomed. Signal Process. Control, vol. 47, pp. 312–323, 2019, doi: https://doi.org/10.1016/j.bspc.2018.08.035.

S. Chatterjee, “A new coefficient of correlation,” J. Am. Stat. Assoc., vol. 116, no. 536, pp. 2009–2022, 2021, doi: https://doi.org/10.1080/01621459.2020.1758115.

A. de Raadt, M. J. Warrens, R. J. Bosker, and H. A. L. Kiers, A comparison of reliability coefficients for ordinal rating scales. Springer, 2021. doi: 10.1007/s00357-021-09386-5.

V. J. Raja, M. Dhanamalar, G. Solaimalai, D. L. Rani, P. Deepa, and R. G. Vidhya, “Machine Learning Revolutionizing Performance Evaluation: Recent Developments and Breakthroughs,” in 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), 2024, pp. 780–785. doi: https://doi.org/10.1109/ICSCSS60660.2024.10625103.




DOI: https://doi.org/10.52088/ijesty.v5i4.1068

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