DECENTRALIZED LEARNING MANAGEMENT SYSTEMS: BLOCKCHAIN APPLICATIONS IN HYBRID EDUCATION GOVERNANCE

Mashuri Mashuri (1), Suhardiman Suhardiman (2), Suhaimi bin Agus (3), Lia Amelia (4)
(1) Sekolah Tinggi Agama Islam Al-Azhar Pekanbaru, Indonesia,
(2) Institut Agama Islam Miftahul Ulum Tanjung Pinang, Indonesia,
(3) Institut Agama Islam Rokan, Indonesia,
(4) Sekolah Tinggi Agama Islam Al-Azhar Pekanbaru, Indonesia

Abstract

The integration of blockchain technology into hybrid education has the potential to revolutionize the governance of Learning Management Systems (LMS). Traditional LMS often suffer from centralization, which leads to issues such as data security vulnerabilities, inefficiencies in administrative processes, and lack of transparency. Blockchain’s decentralized and immutable nature offers a promising solution to these challenges. This study explores the application of blockchain technology in decentralized LMS to enhance governance, transparency, and security in hybrid education environments. The primary aim of the research is to assess how blockchain can be used to improve administrative processes such as grading, credential verification, and accreditation in hybrid learning systems. A qualitative case study approach was used, involving interviews with administrators, educators, and technology experts, as well as document analysis from institutions implementing blockchain-based LMS. The findings reveal that blockchain integration significantly improves data security, reduces administrative delays, and fosters greater trust among stakeholders. Institutions with blockchain-based LMS reported faster processing of student records and a more transparent grading system. The study concludes that blockchain offers a transformative approach to hybrid education governance by creating more secure, transparent, and efficient systems.

Full text article

Generated from XML file

References

Abdelsalam, M., Shokry, M., & Idrees, A. M. (2024). A Proposed Model for Improving the Reliability of Online Exam Results Using Blockchain. IEEE Access, 12, 7719–7733. https://doi.org/10.1109/ACCESS.2023.3304995

Al-Ismail, F. S. (2024). A Critical Review on DC Microgrids Voltage Control and Power Management. IEEE Access, 12, 30345–30361. https://doi.org/10.1109/ACCESS.2024.3369609

Al-Matari, N. Y., Zahary, A. T., & A. Al-Shargabi, A. (2024). A survey on advancements in blockchain-enabled spectrum access security for 6G cognitive radio IoT networks. Scientific Reports, 14(1), 30990. https://doi.org/10.1038/s41598-024-82126-y

Arunan, A., Qin, Y., Li, X., & Yuen, C. (2024). A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction. IEEE Transactions on Automation Science and Engineering, 21(3), 3065–3079. https://doi.org/10.1109/TASE.2023.3274648

Boumaiza, A. (2024). A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade. Energies, 17(4), 915. https://doi.org/10.3390/en17040915

Ghafouri, N., Vardakas, J. S., Ramantas, K., & Verikoukis, C. (2024). A Multi-Level Deep RL-Based Network Slicing and Resource Management for O-RAN-Based 6G Cell-Free Networks. IEEE Transactions on Vehicular Technology, 73(11), 17472–17484. https://doi.org/10.1109/TVT.2024.3415656

Huo, X., Huang, H., Davis, K. R., Poor, H. V., & Liu, M. (2025). A review of scalable and privacy-preserving multi-agent frameworks for distributed energy resources. Advances in Applied Energy, 17, 100205. https://doi.org/10.1016/j.adapen.2024.100205

Ibrahim, A. H., & Al-Homidan, S. (2024). Two-step inertial derivative-free projection method for solving nonlinear equations with application. Journal of Computational and Applied Mathematics, 451, 116071. https://doi.org/10.1016/j.cam.2024.116071

Jamal, M., Ullah, Z., Naeem, M., Abbas, M., & Coronato, A. (2024). A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks. Future Internet, 16(5), 152. https://doi.org/10.3390/fi16050152

Judge, M. A., Franzitta, V., Curto, D., Guercio, A., Cirrincione, G., & Khattak, H. A. (2024). A comprehensive review of artificial intelligence approaches for smart grid integration and optimization. Energy Conversion and Management: X, 24, 100724. https://doi.org/10.1016/j.ecmx.2024.100724

Kalasampath, K., Spoorthi, K. N., Sajeev, S., Kuppa, S. S., Ajay, K., & Maruthamuthu, A. (2025). A Literature Review on Applications of Explainable Artificial Intelligence (XAI). IEEE Access, 13, 41111–41140. https://doi.org/10.1109/ACCESS.2025.3546681

Khan, A. A., Laghari, A. A., Inam, S. A., Ullah, S., & Nadeem, L. (2025). A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology. Discover Sustainability, 6(1), 273. https://doi.org/10.1007/s43621-025-01124-w

Kim, B., Kim, J. G., & Lee, S. (2024). A multi-agent reinforcement learning model for inventory transshipments under supply chain disruption. IISE Transactions, 56(7), 715–728. https://doi.org/10.1080/24725854.2023.2217248

Kopic, A., Perenda, E., & Gacanin, H. (2024). A Collaborative Multi-Agent Deep Reinforcement Learning-Based Wireless Power Allocation With Centralized Training and Decentralized Execution. IEEE Transactions on Communications, 72(11), 7006–7016. https://doi.org/10.1109/TCOMM.2024.3409530

Korba, A. A., Boualouache, A., & Ghamri-Doudane, Y. (2024). Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV. IEEE Transactions on Vehicular Technology, 73(9), 12399–12414. https://doi.org/10.1109/TVT.2024.3385916

Lami, B., Alsolami, M., Alferidi, A., & Slama, S. B. (2025). A Smart Microgrid Platform Integrating AI and Deep Reinforcement Learning for Sustainable Energy Management. Energies, 18(5), 1157. https://doi.org/10.3390/en18051157

Li, K., Li, C., Yuan, X., Li, S., Zou, S., Sohail Ahmed, S., Ni, W., Niyato, D., Jamalipour, A., Dressler, F., & Akan, Ö. B. (2025). Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things. IEEE Internet of Things Journal, 12(22), 46269–46293. https://doi.org/10.1109/JIOT.2025.3603957

Meese, C., Chen, H., Li, W., Lee, D., Guo, H., Shen, C.-C., & Nejad, M. (2024). Adaptive Traffic Prediction at the ITS Edge With Online Models and Blockchain-Based Federated Learning. IEEE Transactions on Intelligent Transportation Systems, 25(9), 10725–10740. https://doi.org/10.1109/TITS.2024.3391053

Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., ?obodzi?ska, A., & ?nieg, M. (2025). The IoT and AI in Agriculture: The Time Is Now A Systematic Review of Smart Sensing Technologies. Sensors, 25(12), 3583. https://doi.org/10.3390/s25123583

Minchala-Ávila, C., Arévalo, P., & Ochoa-Correa, D. (2025). A Systematic Review of Model Predictive Control for Robust and Efficient Energy Management in Electric Vehicle Integration and V2G Applications. Modelling, 6(1), 20. https://doi.org/10.3390/modelling6010020

Perera, L., Ranaweera, P., Kusaladharma, S., Wang, S., & Liyanage, M. (2024). A Survey on Blockchain for Dynamic Spectrum Sharing. IEEE Open Journal of the Communications Society, 5, 1753–1802. https://doi.org/10.1109/OJCOMS.2024.3376233

Priya, S. S., Vijayabhasker, R., & Rajaram, A. (2025). Advanced Security and Efficiency Framework for Mobile Ad-Hoc Networks Using Adaptive Clustering and Optimization Techniques. Journal of Electrical Engineering & Technology, 20(3), 1815–1826. https://doi.org/10.1007/s42835-024-02119-9

Qin, L., Lu, H., Chen, Y., Chong, B., & Wu, F. (2024). Toward Decentralized Task Offloading and Resource Allocation in User-Centric MEC. IEEE Transactions on Mobile Computing, 23(12), 11807–11823. https://doi.org/10.1109/TMC.2024.3399766

Rashid, M. M., Xiang, Y., Uddin, M. P., Tang, J., Sood, K., & Gao, L. (2025). Trustworthy and Fair Federated Learning via Reputation-Based Consensus and Adaptive Incentives. IEEE Transactions on Information Forensics and Security, 20, 2868–2882. https://doi.org/10.1109/TIFS.2025.3546841

Ratta, P., Abdullah, & Sharma, S. (2024). A blockchain-machine learning ecosystem for IoT-Based remote health monitoring of diabetic patients. Healthcare Analytics, 5, 100338. https://doi.org/10.1016/j.health.2024.100338

Sanjalawe, Y., Fraihat, S., Al-E’Mari, S., Abualhaj, M., Makhadmeh, S., & Alzubi, E. (2025). A Review of 6G and AI Convergence: Enhancing Communication Networks With Artificial Intelligence. IEEE Open Journal of the Communications Society, 6, 2308–2355. https://doi.org/10.1109/OJCOMS.2025.3553302

Soltoggio, A., Ben-Iwhiwhu, E., Braverman, V., Eaton, E., Epstein, B., Ge, Y., Halperin, L., How, J., Itti, L., Jacobs, M. A., Kantharaju, P., Le, L., Lee, S., Liu, X., Monteiro, S. T., Musliner, D., Nath, S., Panda, P., Peridis, C., … Kolouri, S. (2024). A collective AI via lifelong learning and sharing at the edge. Nature Machine Intelligence, 6(3), 251–264. https://doi.org/10.1038/s42256-024-00800-2

Wang, G., Qin, R., Li, J., Wang, F.-Y., Gan, Y., & Yan, L. (2024). A Novel DAO-Based Parallel Enterprise Management Framework in Web3 Era. IEEE Transactions on Computational Social Systems, 11(1), 839–848. https://doi.org/10.1109/TCSS.2023.3239059

Witharama, W. M. N., Bandara, K. M. D. P., Azeez, M. I., Bandara, K., Logeeshan, V., & Wanigasekara, C. (2024). Advanced Genetic Algorithm for Optimal Microgrid Scheduling Considering Solar and Load Forecasting, Battery Degradation, and Demand Response Dynamics. IEEE Access, 12, 83269–83284. https://doi.org/10.1109/ACCESS.2024.3412914

Authors

Mashuri Mashuri
mashuristaialazharpku16@gmail.com (Primary Contact)
Suhardiman Suhardiman
Suhaimi bin Agus
Lia Amelia
Mashuri, M., Suhardiman, S., Agus, S. bin ., & Amelia, L. . (2026). DECENTRALIZED LEARNING MANAGEMENT SYSTEMS: BLOCKCHAIN APPLICATIONS IN HYBRID EDUCATION GOVERNANCE. Journal Neosantara Hybrid Learning, 4(2), 101–112. https://doi.org/10.70177/jnhl.v4i2.3775

Article Details