BRAIN-BASED LEARNING STRATEGIES FOR PERSONALIZED HYBRID EDUCATION

Raul Gomez (1), Clara Mendes (2), Rudy Surbakti (3)
(1) Universidade Federal Minas GeraisBR Brazil,
(2) Universidade Estadual CampinasBR Brazil,
(3) STMIK Kristen NeumannID Indonesia

Abstract

Personalized hybrid education offers flexible learning opportunities, yet technological adaptation alone often fails to address differences in attention, memory, cognitive load, prior knowledge, and self-regulation that shape individual learning. This study examined the effectiveness of evidence-informed brain-based learning strategies within personalized hybrid education and developed an adaptive framework for cognitively responsive instruction. A quasi-experimental mixed-methods design compared students receiving brain-informed personalized hybrid instruction with those experiencing conventional hybrid learning. The intervention integrated prior-knowledge activation, retrieval practice, spaced learning, cognitive-load management, adaptive scaffolding, formative feedback, and metacognitive reflection across physical and digital environments. Findings indicated stronger academic achievement, knowledge retention, cognitive engagement, motivation, and self-regulated learning among students receiving personalized instruction, accompanied by more manageable cognitive demands. Learning analytics and qualitative evidence further demonstrated that effective personalization required dynamic adjustments based on learner readiness, performance, progress, and support needs rather than fixed learning-style classifications. The study concludes that brain-informed personalization is most effective when grounded in credible learning science and implemented through continuous cycles of diagnosis, adaptation, retrieval, feedback, monitoring, and recalibration. The proposed framework advances personalized hybrid education by integrating cognitive responsiveness, pedagogical orchestration, and adaptive support while avoiding unsupported neuromyth-based instructional assumptions in contemporary educational practice.

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References

Akter, A., Nosheen, N., Ahmed, S., Hossain, M., Yousuf, M. A., Almoyad, M. A. A., Hasan, K. F., & Moni, M. A. (2024). Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor. Expert Systems with Applications, 238, 122347. https://doi.org/10.1016/j.eswa.2023.122347

Albalawi, E., Thakur, A., Dorai, D. R., Bhatia Khan, S., Mahesh, T. R., Almusharraf, A., Aurangzeb, K., & Anwar, M. S. (2024). Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach. Frontiers in Computational Neuroscience, 18, 1418546. https://doi.org/10.3389/fncom.2024.1418546

Ali, M. U., Zafar, A., Kallu, K. D., Masood, H., Mannan, M. M. N., Ibrahim, M. M., Kim, S., & Khan, M. A. (2024). Correlation-Filter-Based Channel and Feature Selection Framework for Hybrid EEG-fNIRS BCI Applications. IEEE Journal of Biomedical and Health Informatics, 28(6), 3361–3370. https://doi.org/10.1109/JBHI.2023.3294586

Arshad Choudhry, I., Iqbal, S., Alhussein, M., Aurangzeb, K., Qureshi, A. N., & Hussain, A. (2025). A Novel Interpretable Graph Convolutional Neural Network for Multimodal Brain Tumor Segmentation. Cognitive Computation, 17(1), 24. https://doi.org/10.1007/s12559-024-10387-w

Atitallah, S. B., Driss, M., Boulila, W., & Koubaa, A. (2026). Graph-based EEG analysis for seizure prediction enhanced with Kolmogorov–Arnold Networks and Self-Supervised Learning. Engineering Science and Technology, an International Journal, 73, 102245. https://doi.org/10.1016/j.jestch.2025.102245

Bagherzadeh, S., Shalbaf, A., Shoeibi, A., Jafari, M., Tan, R.-S., & Acharya, U. R. (2024). Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps. IEEE Access, 12, 50949–50965. https://doi.org/10.1109/ACCESS.2024.3384303

Ch Vidyasagar, K. E., Revanth Kumar, K., Anantha Sai, G. N. K., Ruchita, M., & Saikia, M. J. (2024). Signal to Image Conversion and Convolutional Neural Networks for Physiological Signal Processing: A Review. IEEE Access, 12, 66726–66764. https://doi.org/10.1109/ACCESS.2024.3399114

Chaki, J., & Wo?niak, M. (2024). Deep Learning and Artificial Intelligence in Action (2019–2023): A Review on Brain Stroke Detection, Diagnosis, and Intelligent Post-Stroke Rehabilitation Management. IEEE Access, 12, 52161–52181. https://doi.org/10.1109/ACCESS.2024.3383140

Chattopadhyay, T., Senthilkumar, P., Ankarath, R. H., Patterson, C., Gleave, E. J., Thomopoulos, S. I., Huang, H., Shen, L., You, L., Zhi, D., & Thompson, P. M. (2026). Deep learning to predict future cognitive decline: A multimodal approach using brain MRI and clinical data. Frontiers in Neuroimaging, 5, 1726037. https://doi.org/10.3389/fnimg.2026.1726037

Chen, L., Lou, H., Yue, P., & Chen, J. (2025). Using fMRI-Based Multi-Scale Perception Models to Explore Cognitive Load and Attention Allocation in Education. IEEE Access, 13, 113648–113665. https://doi.org/10.1109/ACCESS.2025.3571711

Cui, J. (2025). An adaptive hand exoskeleton rehabilitation training system integrating virtual reality and an AI-based assessment engine. Frontiers in Sports and Active Living, 7, 1724021. https://doi.org/10.3389/fspor.2025.1724021

Da, T. N., Cho, M., & Thanh, P. N. (2024). Hourly load prediction based feature selection scheme and hybrid CNN?LSTM method for building’s smart solar microgrid. Expert Systems, 41(7), e13539. https://doi.org/10.1111/exsy.13539

Dan, J., Pale, U., Amirshahi, A., Cappelletti, W., Ingolfsson, T. M., Wang, X., Cossettini, A., Bernini, A., Benini, L., Beniczky, S., Atienza, D., & Ryvlin, P. (2025). SZCORE: Seizure Community Open?Source Research Evaluation framework for the validation of ELECTROENCEPHALOGRAPHY ?based automated seizure detection algorithms. Epilepsia, 66(S3), 14–24. https://doi.org/10.1111/epi.18113

Del Pup, F., Zanola, A., Tshimanga, L. F., Bertoldo, A., Finos, L., & Atzori, M. (2025). The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study. Computers in Biology and Medicine, 196, 110608. https://doi.org/10.1016/j.compbiomed.2025.110608

Devendiran, R., & Turukmane, A. V. (2024). Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy. Expert Systems with Applications, 245, 123027. https://doi.org/10.1016/j.eswa.2023.123027

Ghahramani, M., & Bavi, O. (2026). Biomechanical modeling of glioblastoma progression: A comprehensive review from classic mathematical frameworks to data-driven strategies. Biomechanics and Modeling in Mechanobiology, 25(1), 1. https://doi.org/10.1007/s10237-025-02028-4

Huang, Y., Cheung, C. Y., Li, D., Tham, Y. C., Sheng, B., Cheng, C. Y., Wang, Y. X., & Wong, T. Y. (2024). AI-integrated ocular imaging for predicting cardiovascular disease: Advancements and future outlook. Eye, 38(3), 464–472. https://doi.org/10.1038/s41433-023-02724-4

Hussein, N. A.-H. K., & Jbara, W. A. (2025). Enhancing brain tumor classification through deep learning-based analysis of genetic data. 050104. https://doi.org/10.1063/5.0258434

Keutayeva, A., & Abibullaev, B. (2024). Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey. IEEE Access, 12, 62628–62647. https://doi.org/10.1109/ACCESS.2024.3394696

Khoshfekr Rudsari, H., Tseng, B., Zhu, H., Song, L., Gu, C., Roy, A., Irajizad, E., Butner, J., Long, J., & Do, K.-A. (2025). Digital twins in healthcare: A comprehensive review and future directions. Frontiers in Digital Health, 7, 1633539. https://doi.org/10.3389/fdgth.2025.1633539

Kina, E. (2025). TLEABLCNN: Brain and Alzheimer’s Disease Detection Using Attention-Based Explainable Deep Learning and SMOTE Using Imbalanced Brain MRI. IEEE Access, 13, 27670–27683. https://doi.org/10.1109/ACCESS.2025.3539550

Kuo, P.-C., Chou, Y.-T., Li, K.-Y., Chang, W.-T., Huang, Y.-N., & Chen, C.-S. (2024). GNN-LSTM-based fusion model for structural dynamic responses prediction. Engineering Structures, 306, 117733. https://doi.org/10.1016/j.engstruct.2024.117733

Lamba, K., Rani, S., & Shabaz, M. (2025). Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare. Scientific Reports, 15(1), 20489. https://doi.org/10.1038/s41598-025-07524-2

Li, Z., Yao, S., Chen, D., Li, L., Lu, Z., Liu, W., & Yu, Z. (2024). Multi-parameter co-optimization for NOx emissions control from waste incinerators based on data-driven model and improved particle swarm optimization. Energy, 306, 132477. https://doi.org/10.1016/j.energy.2024.132477

Murugan, K., Palanisamy, S., Sathishkumar, N., & Alshalali, T. A. N. (2025). Advanced finite segmentation model with hybrid classifier learning for high-precision brain tumor delineation in PET imaging. Scientific Reports, 15(1), 25666. https://doi.org/10.1038/s41598-025-09638-z

Priyadarshini, P., Kanungo, P., & Kar, T. (2024). Multigrade brain tumor classification in MRI images using Fine tuned efficientnet. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 8, 100498. https://doi.org/10.1016/j.prime.2024.100498

Rahman, T., Islam, M. S., & Uddin, J. (2024). MRI-Based Brain Tumor Classification Using a Dilated Parallel Deep Convolutional Neural Network. Digital, 4(3), 529–554. https://doi.org/10.3390/digital4030027

Reddy, B. S., Jha, R. R., Dasore, A., Desur, D., Shahapurkar, K., Tirth, V., Algahtani, A., Bhaviripudi, V. R., & Gebremaryam, G. (2026). Multi-class classification of brain tumor using a ResNet101 backbone integrated with multi-scale deformable attention module and advanced data augmentations. Scientific Reports, 16(1), 15938. https://doi.org/10.1038/s41598-026-45675-y

Saranya, S., & Menaka, R. (2025). A Quantum-Based Machine Learning Approach for Autism Detection Using Common Spatial Patterns of EEG Signals. IEEE Access, 13, 15739–15750. https://doi.org/10.1109/ACCESS.2025.3531979

Song, J., Zhai, Q., Wang, C., & Liu, J. (2024). EEGGAN-Net: Enhancing EEG signal classification through data augmentation. Frontiers in Human Neuroscience, 18, 1430086. https://doi.org/10.3389/fnhum.2024.1430086

Tang, S., Han, E. L., & Mitchell, M. J. (2026). Peptide-functionalized nanoparticles for brain-targeted therapeutics. Drug Delivery and Translational Research, 16(3), 741–760. https://doi.org/10.1007/s13346-025-01840-w

Ting, H., & Liu, M. (2024). Multimodal Transformer of Incomplete MRI Data for Brain Tumor Segmentation. IEEE Journal of Biomedical and Health Informatics, 28(1), 89–99. https://doi.org/10.1109/JBHI.2023.3286689

Umirzakova, S., Shakhnoza, M., Sevara, M., & Whangbo, T. K. (2025). Deep learning for multiple sclerosis lesion classification and stratification using MRI. Computers in Biology and Medicine, 192, 110078. https://doi.org/10.1016/j.compbiomed.2025.110078

Uthamacumaran, A. (2025). Deep learning-based feature discovery for decoding phenotypic plasticity in pediatric high-grade gliomas single-cell transcriptomics. Computers in Biology and Medicine, 197, 110971. https://doi.org/10.1016/j.compbiomed.2025.110971

Volov??, C. C., Buzea, C. G., Boboc, D.-I., Ostafe, M.-R., Agop, M., Ochiuz, L., Burlea, ?tefan L., Rusu, D. I., Bujor, L., Iancu, D. T., & Volov??, S. R. (2025). Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data. Diagnostics, 15(10), 1242. https://doi.org/10.3390/diagnostics15101242

Volov??, S. R., Popa, T. O., Rusu, D., Ochiuz, L., Vasincu, D., Agop, M., Buzea, C. G., & Volov??, C. C. (2024). Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics. Diagnostics, 14(18), 2091. https://doi.org/10.3390/diagnostics14182091

Wankhede, D. S., Shahade, A. K., Deshmukh, P. V., Manikjade, A., Shahade, M., Gohatre, P. H., & Tidke, K. (2025). Deep Neural Network-Based Risk Prediction of Glioblastoma Multiforme Recurrence. Journal of Molecular Neuroscience, 75(4), 132. https://doi.org/10.1007/s12031-025-02412-w

Xie, X., Zhang, X., Tang, X., Zhao, J., Xiong, D., Ouyang, L., Yang, B., Zhou, H., Ling, B. W.-K., & Teo, K. L. (2025). MACTFusion: Lightweight Cross Transformer for Adaptive Multimodal Medical Image Fusion. IEEE Journal of Biomedical and Health Informatics, 29(5), 3317–3328. https://doi.org/10.1109/JBHI.2024.3391620

Xu, Q., Lu, J., Zhang, Z., Xu, D., & Guo, C. (2026). Deep learning–based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms. Neural Regeneration Research, 21(9), 4135–4147. https://doi.org/10.4103/NRR.NRR-D-25-00332

Xu, Y., Yang, J., Ming, W., Wang, S., & Sawan, M. (2024). Shorter latency of real-time epileptic seizure detection via probabilistic prediction. Expert Systems with Applications, 236, 121359. https://doi.org/10.1016/j.eswa.2023.121359

Xu, Y., Zhang, Z., & Feng, K. (2026). Spatiotemporal video of blood-brain barrier disruption in neuroinflammatory disorders. Frontiers in Physiology, 16, 1633126. https://doi.org/10.3389/fphys.2025.1633126

Yao, Q., Zhuang, D., Feng, Y., Wang, Y., & Liu, J. (2024). Accurate Detection of Brain Tumor Lesions From Medical Images Based on Improved YOLOv8 Algorithm. IEEE Access, 12, 144260–144279. https://doi.org/10.1109/ACCESS.2024.3472039

Yu, D., Chaoyi, D., Xu, Z., Gangqiang, C., Hongxin, Y., Junting, L., & Xiaoyan, C. (2025). An Adaptive Filterbank Graph Convolution Network for Motor Imagery EEG Decoding. IFAC-PapersOnLine, 59(35), 661–666. https://doi.org/10.1016/j.ifacol.2025.12.554

Zhang, H., Zuo, T., Chen, Z., Wang, X., & Sun, P. Z. H. (2024). Evolutionary Ensemble Learning for EEG-Based Cross-Subject Emotion Recognition. IEEE Journal of Biomedical and Health Informatics, 28(7), 3872–3881. https://doi.org/10.1109/JBHI.2024.3384816

Zhang, Z., Wen, Y., Liang, W., Zhang, F., Niu, G., & Ma, Q. (2026). Mamba-HTEPA: A multi-branch structure framework for multimodal grading of meningiomas using Mamba. Expert Systems with Applications, 326, 132689. https://doi.org/10.1016/j.eswa.2026.132689

Zou, J., Lou, J., Wang, B., & Liu, S. (2024). A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networks. Expert Systems with Applications, 242, 122801. https://doi.org/10.1016/j.eswa.2023.122801

Authors

Raul Gomez
raulgomez@gmail.com (Primary Contact)
Clara Mendes
Rudy Surbakti
Gomez, R. ., Mendes, C. ., & Surbakti, R. . (2026). BRAIN-BASED LEARNING STRATEGIES FOR PERSONALIZED HYBRID EDUCATION. Journal Neosantara Hybrid Learning, 4(3), 173–192. https://doi.org/10.70177/jnhl.v4i3.4233

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