A NEUROEDUCATION-INFORMED DESIGN FOR A HYBRID LEARNING MODULE TO OPTIMIZE COGNITIVE LOAD AND MEMORY RETENTION
Article Sidebar
-
Cognitive Load, Hybrid Learning, Instructional Design
Abstract
The growing complexity of hybrid learning environments demands instructional models that align with the cognitive architecture of the human brain. Excessive cognitive load and fragmented attention have become significant barriers to effective learning in digital and blended modalities. Neuroeducation—the interdisciplinary integration of neuroscience, psychology, and pedagogy—offers empirical insights for designing learning experiences that optimize working memory, enhance attention, and improve long-term retention. This study aims to develop and evaluate a neuroeducation-informed hybrid learning module that systematically manages cognitive load and facilitates memory consolidation among university students. A quasi-experimental design was employed with two groups: an experimental class using the neuroeducation-informed module and a control class using a conventional hybrid model. Participants included 80 undergraduate students enrolled in an educational psychology course. Data were collected through cognitive load scales, memory recall tests, and observational field notes, complemented by EEG-based attention tracking in a subsample. Quantitative data were analyzed using ANOVA, while qualitative data underwent thematic coding to identify engagement and retention patterns. The results indicated that the experimental group experienced significantly lower extraneous cognitive load (p < 0.01) and achieved higher delayed recall scores (M = 82.4) than the control group (M = 68.7). Students also reported improved focus, motivation, and conceptual understanding. The study concludes that integrating neuroeducation principles—such as chunking, spaced repetition, multimodal encoding, and emotional relevance—can substantially enhance hybrid learning effectiveness. The proposed framework bridges cognitive science and instructional design, contributing to sustainable innovation in higher education pedagogy.
Full text article
References
IAbusafieh, S. F. (2025). Empowering digital transformation in higher education: Assessing the effectiveness of asynchronous online learning in architecture pedagogy. Open House International, 50(5), 928–942. https://doi.org/10.1108/OHI-01-2025-0013
Adamu, A. A., Sathappan, M., Adeyinka-Ojo, S. F., Sathappan, R., Ahmad, G. I., & Kalam, A. (2025). Effectiveness and learning outcomes of hybrid learning in Malaysian undergraduate courses. Asian Education and Development Studies, 14(5), 960–978. https://doi.org/10.1108/AEDS-07-2024-0157
Alkam, T., Tarshizi, E., & Van Benschoten, A. H. (2025). Reinforcement learning at the interface of artificial intelligence and cognitive science. Neuroscience, 585, 289–312. https://doi.org/10.1016/j.neuroscience.2025.09.004
Al-Qaysi, N., Al-Emran, M., Al-Sharafi, M. A., Yaseen, Z. M., Mahmoud, M. A., & Ahmad, A. (2025). Generative AI and educational sustainability: Examining the role of knowledge management factors and AI attributes using a deep learning-based hybrid SEM-ANN approach. Computer Standards & Interfaces, 93, 103964. https://doi.org/10.1016/j.csi.2024.103964
Azeez, F., & Aboobaker, N. (2024). Exploring new frontiers of experiential learning landscape: A hybrid review. The Learning Organization, 31(6), 985–1007. https://doi.org/10.1108/TLO-02-2023-0022
Balabadruni Venkata, N. koushik, D, S., Ganesh Reddy, G. S. L., & Potturi, P. (2025). Eye gaze-controlled camera navigation for enhanced robotic surgery with potential Cognitive Load reduction. Current Problems in Surgery, 70, 101846. https://doi.org/10.1016/j.cpsurg.2025.101846
Bao, C., Xu, Z., & Ji, W. (2025). CQSA-KT: Research on personalized knowledge tracing based on quantum-constructivism in sparse learning environments. Knowledge-Based Systems, 330, 114493. https://doi.org/10.1016/j.knosys.2025.114493
Belhaj, R., Ali, N. A., & Browlahrouf, I. (2025). Gamified Hybrid Learning for Neurodiverse Students: Designing Universally Accessible Instructional Models. Journal Neosantara Hybrid learning , 3(1), 38–46. https://doi.org/10.70177/jnhl.v3i1.2229
Bhattacharya, R., Bose, D., Rodriguez, R. V., K, H., & Siddique, K. R. (2025). Personalized multi-modal interfaces for cognitive aging: A narrative review of design and technological innovations. Archives of Gerontology and Geriatrics Plus, 2(4), 100206. https://doi.org/10.1016/j.aggp.2025.100206
Candido, V., Cattaneo, A., & Petko, D. (2025). Exploring multimedia principles and learning from errors in augmented reality and video. Learning and Instruction, 101, 102244. https://doi.org/10.1016/j.learninstruc.2025.102244
CHEN, F., XIONG, B., & CHEN, G. (2025). Fostering scientific inquiry with hybrid intelligence: A semester-long experiment on knowledge acquisition in the science classroom. Computers & Education, 105493. https://doi.org/10.1016/j.compedu.2025.105493
Dordevic, J., Stojanovi, L., & Markovi, T. (2025). Blockchain-Based Academic Records for Hybrid Education: Securing Digital Credentials in Global Crisis Contexts. Journal Neosantara Hybrid learning , 3(1), 20–28. https://doi.org/10.70177/jnhl.v3i1.2231
Fang, H., Hu, Y., Chen, S., Yang, X., Zhao, Y., Niu, H., & Cai, C. (2025). Effects of interface design and spatial ability on teleoperation Cognitive Load and task performance. Displays, 87, 102977. https://doi.org/10.1016/j.displa.2025.102977
Farrokhnia, M., Noroozi, O., Baggen, Y., Biemans, H., & Weinberger, A. (2025). Improving hybrid brainstorming outcomes with computer-supported scaffolds: Prompts and cognitive group awareness. Computers & Education, 227, 105229. https://doi.org/10.1016/j.compedu.2024.105229
Gao, S., Zhang, L., Shangguan, F., & Yang, J. (2024). The effect of OMO learning on high school students’ problem-solving skills in physics. Thinking Skills and Creativity, 54, 101674. https://doi.org/10.1016/j.tsc.2024.101674
Garavandala, U. S. (2025). Neuroscience-driven workforce architecture: Mapping Cognitive Load to role design. Strategic HR Review, 24(6), 243–252. https://doi.org/10.1108/SHR-05-2025-0050
Göndöcs, D., Horváth, S., & Dörfler, V. (2025). Uncovering the dynamics of human-AI hybrid performance: A qualitative meta-analysis of empirical studies. International Journal of Human-Computer Studies, 205, 103622. https://doi.org/10.1016/j.ijhcs.2025.103622
Gonzales, S., Fernandez, C., & Wei, L. (2025). Adopting Heutagogy in Non-Formal Education: A Participatory Action Research with Community Learning Centers. Journal Neosantara Hybrid learning , 3(1), 29–37. https://doi.org/10.70177/jnhl.v3i1.2186
Hu, W., & Shao, Z. (2025). Design and evaluation of a GenAI-based personalized educational content system tailored to personality traits and emotional responses for adaptive learning. Computers in Human Behavior Reports, 19, 100735. https://doi.org/10.1016/j.chbr.2025.100735
Huang, J., Xu, X., & Polzer, J. (2025). Towards instructional collaborative robots: From video-based learning to feedback-adapted instruction. Journal of Manufacturing Systems, 83, 29–45. https://doi.org/10.1016/j.jmsy.2025.08.018
Jana, A. H. A., Gaffinet, B., Lezoche, M., Panetto, H., & Naudet, Y. (2025). Enabling human–CPS cognitive interoperability: Cognitive architectures as technologies for human-like cognitive digital twins. Journal of Industrial Information Integration, 48, 100969. https://doi.org/10.1016/j.jii.2025.100969
Jiao, J. (2025). An Empirical Study on the Online English Learning Effect of College Students From the Perspective of Learning Analysis. International Journal of Web-Based Learning and Teaching Technologies, 20(1). https://doi.org/10.4018/IJWLTT.383085
Jo, T. H., Han, H., & Hyun, K. H. (2025). Enhancing consumer perceptions and experiences through hybrid-based interfaces in virtual environments. Electronic Commerce Research and Applications, 74, 101551. https://doi.org/10.1016/j.elerap.2025.101551
Kayande, D., & Kukreja, S. (2025). Design of an integrated multi-modal machine learning framework for real-time student engagement evaluation and learning outcome optimizations. MethodsX, 15, 103588. https://doi.org/10.1016/j.mex.2025.103588
Keulemans, T., Depaepe, F., & Raes, A. (2025). Lightening the load: Unveiling the factors influencing teacher orchestration load in synchronous hybrid education. Learning and Instruction, 99, 102189. https://doi.org/10.1016/j.learninstruc.2025.102189
Khan, M. A., Asadi, H., Zhang, L., Qazani, M. R. C., Oladazimi, S., Loo, C. K., Lim, C. P., & Nahavandi, S. (2024). Application of artificial intelligence in Cognitive Load analysis using functional near-infrared spectroscopy: A systematic review. Expert Systems with Applications, 249, 123717. https://doi.org/10.1016/j.eswa.2024.123717
K?yak, Y. S., & Kononowicz, A. A. (2025). Using a Hybrid of AI and Template-Based Method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: Hybrid AIG. JMIR Formative Research, 9. https://doi.org/10.2196/65726
Lavoie, P., Lapierre, A., Maheu-Cadotte, M.-A., Brien, L.-A., Ledoux, I., & Gosselin, É. (2024). Nursing Students’ Engagement in Virtual Reality and Hybrid Simulations: A Quasi-Experimental Study. Clinical Simulation in Nursing, 87, 101496. https://doi.org/10.1016/j.ecns.2023.101496
Liu, S., Liu, T., Alqurashi, M., Abdou Elabbasy, A. A., Alanazi, N., & Shakor, P. (2025). Advancing 3D-printed fiber-reinforced concrete for sustainable construction: A comparative optimization based study of hybrid machine intelligence models for predicting mechanical strength and CO? emissions. Case Studies in Construction Materials, 23, e05259. https://doi.org/10.1016/j.cscm.2025.e05259
Low, H.-C., Mohamad, F. S., Mujahid, A., & Khan, R. U. (2025). A preliminary study on diagnosing learning difficulties in cell division topic for mGBL design and development. Social Sciences & Humanities Open, 12, 102030. https://doi.org/10.1016/j.ssaho.2025.102030
Lu, D. (2025). Supporting genre-based writing instruction in EAP with a blended collaborative learning environment: A case study of a graduate-level EFL academic writing course. Journal of English for Academic Purposes, 78, 101589. https://doi.org/10.1016/j.jeap.2025.101589
Mahalingam, M. S., Kumar, N. S., Devi, R. K., & Logeshwaran, J. (2025). A reliable inter-domain routing framework for autonomous systems using hybrid Blockchain. Computers and Electrical Engineering, 123, 110031. https://doi.org/10.1016/j.compeleceng.2024.110031
Mahmoudi-Dehaki, M., & Nasr-Esfahani, N. (2025). Empowering stuttering female English learners: AI vs. Human-AI hybrid tutoring for alleviating social anxiety. Journal of Responsible Technology, 24, 100141. https://doi.org/10.1016/j.jrt.2025.100141
Ortega-Arranz, A., Amarasinghe, I., Martínez-Monés, A., Asensio-Pérez, J. I., Dimitriadis, Y., Corrales-Astorgano, M., & Hernández-Leo, D. (2024). Collaborative activities in hybrid learning environments: Exploring teacher orchestration load and students’ perceptions. Computers & Education, 219, 105105. https://doi.org/10.1016/j.compedu.2024.105105
Pan, S., Hafez, B., Iskandar, A., & Ming, Z. (2024). Integrating constructivist principles in an adaptive hybrid learning system for developing social entrepreneurship education among college students. Learning and Motivation, 87, 102023. https://doi.org/10.1016/j.lmot.2024.102023
Pillai, R., Kumar, P., Kumar, N., & Tabash, M. I. (2025). Decoding financial behaviour through cognitive and digital pathways: A hybrid SEM–ANN study among Indian working women. Financial Literacy around the World, 25, 87–100. https://doi.org/10.1016/j.bir.2025.09.007
Poupard, M., Larrue, F., Bertrand, M., Liguoro, D., Tricot, A., & Sauzéon, H. (2025). Using virtual reality for enhancing neuroanatomy learning by optimizing Cognitive Load and intrinsic motivation. Computers & Education, 235, 105332. https://doi.org/10.1016/j.compedu.2025.105332
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Escolar-Llamazares, M.-C., Martín-Antón, L. J., & Velasco-Saiz, R. (2025). Monitoring university students’ learning processes: Application of Advanced Learning Technologies and integrated multichannel techniques. Thinking Skills and Creativity, 58, 101938. https://doi.org/10.1016/j.tsc.2025.101938
Schermeier, S., Deiglmayr, A., & Rummel, N. (2025). Small group collaboration in hybrid university learning: Comparing learners’ perceived socio-affective state in hybrid, face-to-face and remote collaboration. Computers in Human Behavior Reports, 18, 100656. https://doi.org/10.1016/j.chbr.2025.100656
Shaya, N., AbuKhait, R., Madani, R., & Ahmed, V. (2025). Conceptualizing blended learning models as a sustainable and inclusive educational approach: An organizational dynamics perspective. International Journal of Sustainability in Higher Education, 26(9), 90–111. https://doi.org/10.1108/IJSHE-03-2024-0167
Sidorkin, A. M. (2025). Extended executive cognition, a learning outcome for the AI age. Computers and Education Open, 9, 100294. https://doi.org/10.1016/j.caeo.2025.100294
Singh, A. K. (2025). VR/AR in ergonomics and workspace design: A dual-perspective analysis of applications and implications. Applied Ergonomics, 129, 104612. https://doi.org/10.1016/j.apergo.2025.104612
Uslu-Sahan, F., Ozdemir, L., Karadas, M. M., Yildirim, S., & Odabasi, O. (2025). Virtual reality and standardized patient simulation programs in chemotherapy education for breast cancer: A comparative analysis of nursing students’ knowledge, Cognitive Load, satisfaction and confidence. Nurse Education in Practice, 83, 104286. https://doi.org/10.1016/j.nepr.2025.104286
Vann, R., Rith, V., & Suyitno, S. (2025). Community-Based Social Education for Sustainable Development – An Indonesian Perspective on Collaborative Learning Models. Journal Neosantara Hybrid learning , 3(1), 10–19. https://doi.org/10.70177/jnhl.v3i1.2174
Verma, C. (2025). A real-time AI tool for hybrid learning recommendation in education: Preliminary results. Computers and Education: Artificial Intelligence, 8, 100432. https://doi.org/10.1016/j.caeai.2025.100432
Wang, S., Sun, Z., Wang, H., Yang, D., & Zhang, H. (2025). Enhancing student acceptance of artificial intelligence-driven hybrid learning in business education: Interaction between self-efficacy, playfulness, emotional engagement, and university support. The International Journal of Management Education, 23(2), 101184. https://doi.org/10.1016/j.ijme.2025.101184
Wiegand, I., Donkers, I., & Oosterman, J. M. (2025). Individual differences in trait curiosity influence hybrid search speed across the adult lifespan. Acta Psychologica, 260, 105693. https://doi.org/10.1016/j.actpsy.2025.105693
Xue, M., Liu, P., Zhang, J., Sun, Y., Fang, Y., Yang, J., Zeng, C., Huang, B., Chen, X., Xie, M., & Ye, Z. (2024). Does a Video-Based and 3D Animation Hybrid Learning System Improve Teaching Outcomes in Orthopedic Surgery? A Randomized Controlled Trial. Journal of Surgical Education, 81(9), 1305–1319. https://doi.org/10.1016/j.jsurg.2024.05.015
Yan, S., Zhang, F., Pei, A., Guo, X., & Wu, H. (2025). Research on intelligent prediction model of hydraulic support load based on digital twin and multi-agent framework. Results in Engineering, 28, 107182. https://doi.org/10.1016/j.rineng.2025.107182
Zhang, H., Lu, X., Chen, F., Gong, X., & Tan, H. (2025). Inference of takeover time budget for level 3 autonomous vehicles using triboelectric sensors and hybrid learning. Chemical Engineering Journal, 515, 163749. https://doi.org/10.1016/j.cej.2025.163749
Zhang, S., Li, Y., Gan, G., Stevany, Pang, S., & Kim, J. H. (2025). Effects of social cues of artificial intelligence-powered pedagogical agents: A multilevel meta-analysis. Educational Research Review, 49, 100746. https://doi.org/10.1016/j.edurev.2025.100746
Zhongsheng, Z. (2025). Fusion and innovation-advancing sustainability through hybrid musical genres in education. Acta Psychologica, 255, 104851. https://doi.org/10.1016/j.actpsy.2025.104851
Zhu, Y., Wang, X., Su, S., Qiao, Z., Guo, M., Zhang, K., & Dai, Y. (2025). Interpretable hybrid-ensemble acute myocardial infarction diagnosis with clinical transparency. International Journal of Cardiology, 438, 133598. https://doi.org/10.1016/j.ijcard.2025.133598
Zou, J., & Jiang, S. (2025). Adaptive Instructional Designs in blended learning to enhance student engagement and self-regulation. Computers and Education Open, 9, 100299. https://doi.org/10.1016/j.caeo.2025.100299
Authors
Copyright (c) 2026 Lucy Lidiawati Santioso, Samat Yessentayev, Assel Zhanibek, Ayan Nurgaliyev
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
