AI-Based Learning Analytics and Real-Time Monitoring for IoT-Enabled Smart Laboratory of Engineering Education

Haider TH. Salim ALRikabi; Farah Sabir Mohammed; Huda Abbas  Kanber; Meithem Hashem Ali; Hussain Ali Mutar; Ibtihal R. Niama ALRubeei

doi:10.3991/ijep.v16i5.61821

Authors

Haider TH. Salim ALRikabi Wasit University, Wasit, Iraq https://orcid.org/0000-0002-5201-2084
Farah Sabir Mohammed Al-Karkh University of Science, Baghdad, Iraq
Huda Abbas Kanber University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0002-6118-1613
Meithem Hashem Ali Student Registration and Affairs Department, Iraqia University, Baghdad, Iraq https://orcid.org/0009-0004-2253-2144
Hussain Ali Mutar Wasit University, Wasit, Iraq https://orcid.org/0009-0004-4934-416X
Ibtihal R. Niama ALRubeei Wasit University, Wasit, Iraq https://orcid.org/0009-0003-8958-8487

DOI:

https://doi.org/10.3991/ijep.v16i5.61821

Keywords:

Internet of Things, smart laboratory, engineering education, real-time monitoring, learning analytics, edge AI

Abstract

Engineering laboratories play a significant role in facilitating problem-based learning across electrical, environmental, computer, and interdisciplinary engineering programs; however, most lab sessions still rely on physical attendance, delayed grading, limited equipment references, and little visibility into student engagement. This lack of visibility compromises instructors’ ability to identify learning barriers, unsafe conditions, uneven resource use, and at-risk students during lab sessions. An Internet of Things (IoT) smart lab framework is proposed that uses real-time sensor data, edge processing, and cloud storage to support learning analytics for improved operational and pedagogical decisions. The framework integrates data from environmental sensors, smart equipment, workstation logs, access control, and learning platforms to create a continuous data stream. An analytic layer processes this data using structured event learning and sequence modeling to estimate engagement, detect at-risk sessions, identify equipment bottlenecks, and enable timely intervention. A prototype simulated undergraduate engineering lab is developed to evaluate latency, reliability, predictive performance, and interpretability. Results show 93.4% engagement accuracy, 0.914 macro-F1, 0.887 AUROC for early risk detection, and 1.8 seconds median latency, outperforming temporal and non-temporal baselines. These findings demonstrate that multimodal sensing with explainable AI improves operational visibility and educational analytics. The framework defines the engineering lab as a cyber-physical learning environment rather than a collection of devices, supporting safer, more efficient, and data-driven learning across engineering disciplines.

Author Biography

Haider TH. Salim ALRikabi, Wasit University, Wasit, Iraq

Wasit University,College of Engineering,Electrical Engineering Department

References

[1] A. Banagar and R. Khattar, "IoT based Smart Laboratory System," International Journal of Engineering Research and, vol. V9, 01/25 2020, doi: 10.17577/IJERTV9IS010178.

[2] A. Pfeiffer, B. Heinemann, D. Uckelmann, and U. Schroeder, LEARNING ANALYTICS IN ENGINEERING EDUCATION: HOW TO SUPPORT LABORATORY-BASED LEARNING WITH LEARNING ANALYTICS. 2020, pp. 2484-2492.

[3] T. Zuhelmi et al., "Smart Lab Energy – For Real-Time Electricity Consumption Monitoring In The Electronics Engineering Technology Laboratory, Polytechnic State Of Lampung," Electrician : Jurnal Rekayasa dan Teknologi Elektro, vol. 20, pp. 46-55, 01/31 2026, doi: 10.23960/elc.v20n1.2970.

[4] S. Matarneh, L. AlQaraleh, T. Alkhrissat, and M. Abdel-Jaber, "An analysis of E-learning system challenges in engineering education: an empirical study," Cogent Education, vol. 12, no. 1, p. 2445967, 2025/12/31 2025, doi: 10.1080/2331186X.2024.2445967.

[5] C. U. Lei, H. N. Liang, and K. L. Man, "Building a smart laboratory environment at a university via a cyber-physical system," in Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), 26-29 Aug. 2013 2013, pp. 243-247, doi: 10.1109/TALE.2013.6654439.

[6] A. Dhouib, M. H. Fadi Hijab, K. Chemnad, D. Al-Thani, and A. Othman, "Advancing inclusive smart learning environments: a systematic review of technologies, challenges, and best practices for students with disabilities," Smart Learning Environments, vol. 12, no. 1, p. 70, 2025/12/18 2025, doi: 10.1186/s40561-025-00416-y.

[7] M. Bargavi, H. Muhammed, P. Harish, and D. Dhanush, "Edge Computing and AI for Real-time Analytics in Smart Devices," Asian Journal of Basic Science & Research, vol. 7, no. 2, pp. 01-09, 2025, doi: http://doi.org/10.38177/AJBSR.2025.7201.

[8] L. Cabral, R. Pinto, and G. Gonçalves, "AI-powered learning analytics dashboards: a systematic review of applications, techniques, and research gaps," Discover Education, vol. 4, no. 1, p. 525, 2025/11/28 2025, doi: 10.1007/s44217-025-00964-y.

[9] M. M. Asad, A. Naz, A. Shaikh, M. Alrizq, M. Akram, and A. Alghamdi, "Investigating the impact of IoT-Based smart laboratories on students’ academic performance in higher education," Universal Access in the Information Society, vol. 23, no. 3, pp. 1135-1149, 2024/08/01 2024, doi: 10.1007/s10209-022-00944-1.

[10] K. M. Al-Aubidy, "AI and IoT Technologies for Remotely Accessed Laboratories," in 2025 18th International Conference on Development in eSystem Engineering (DeSE), 10-12 Nov. 2025 2025, pp. 302-307, doi: 10.1109/DeSE68208.2025.11367863.

[11] W. Zhang, W. Chen, Q. Wang, J. Ma, and F. Zhang, "Integrating artificial intelligence and the internet of things for smart laboratory engineering construction and management team optimisation," International Journal of Information and Communication Technology, vol. 26, no. 48, pp. 40-59, 2025, doi: https://doi.org/10.1504/IJICT.2025.150949.

[12] S. Amador Nelke et al., "Enhancing Lessons on the Internet of Things in Science, Technology, Engineering, and Medical Education with a Remote Lab," Sensors, vol. 24, no. 19, p. 6424doi: 10.3390/s24196424.

[13] Y. Youssef, J. Schneider, and D. Schiffner, "When and How Do Smart Learning Environments Intervene? A Classification Framework for Context-Aware Learning," in 2025 International Conference on Education Technology and Computers (ICETC), 18-21 Sept. 2025 2025, pp. 832-836, doi: 10.1109/ICETC66579.2025.11387254.

[14] A. Srivastava, A. Reddy, and M. Kk, "IoT-Based Real-Time Monitoring System for Laboratory Hazards," 05/01 2025.

[15] H. A. Mutar, S. M. M. Najeeb, M. L. Aldabag, and H. T. Salim, "Hybrid Neural Network-Genetic Algorithm Framework for EEG and ECG Signal Classification," 2026, doi: DOI:10.53759/7669/jmc202606021.

[16] L. Yan et al., "Evidence-based multimodal learning analytics for feedback and reflection in collaborative learning," British Journal of Educational Technology, vol. 55, no. 5, pp. 1900-1925, 2024/09/01 2024, doi: https://doi.org/10.1111/bjet.13498.

[17] F. Chen and G. Chen, "Learning analytics in inquiry-based learning: a systematic review," Educational technology research and development, vol. 73, no. 4, pp. 2131-2161, 2025/08/01 2025, doi: 10.1007/s11423-025-10507-9.

[18] A. H. Hassan, E. M. Ahmed, J. M. Hussien, R. b. Sulaiman, M. Abdulhak, and H. Kahtan, "A cyber physical sustainable smart city framework toward society 5.0: Explainable AI for enhanced SDGs monitoring," Research in Globalization, vol. 10, p. 100275, 2025/06/01/ 2025, doi: https://doi.org/10.1016/j.resglo.2025.100275.

[19] S. Jayousi, P. Lucattini, L. Petti, F. Bruni, and L. Mucchi, "Smart Learning by Design: A Framework for IoT-Driven Adaptive Classrooms and Inclusive Education," Education Sciences, vol. 15, no. 10, p. 1338doi: 10.3390/educsci15101338.

[20] D. Hasan, "Iot-based smart education: A systematic review of the state of the art," Journal of Intelligent Systems and Information Technology, vol. 2, no. 1, pp. 1-16, 2025, doi: https://doi.org/10.61971/jisit.v2i1.106.

[21] L. Paulsen and E. Lindsay, "Learning analytics dashboards are increasingly becoming about learning and not just analytics - A systematic review," Education and Information Technologies, vol. 29, no. 11, pp. 14279-14308, 2024/08/01 2024, doi: 10.1007/s10639-023-12401-4.

[22] Z. Li, "The Early Warning Model of College Students' Learning Situations Based On Machine Learning," presented at the Proceedings of the 2024 International Conference on Intelligent Education and Computer Technology, Guilin, China, 2024. [Online]. Available: https://doi.org/10.1145/3687311.3687408.

[23] H. A. Mutar, I. R. N. ALRubeei, O. H. Yahya, N. A. Hussien, H. T. S. AlRikabi, and A. H. M. Alaidi, "Auto-PPA: An Adaptive Deep RL Agent for VLSI Physical Design Optimization," Journal of VLSI Circuits and Systems, vol. 8, no. 1, pp. 9-19, 2026, doi: https://doi.org/10.31838/JCVS/08.01.02.

[24] V. Maanu, F. Boateng, and E. Larbi, "Comparison of AI-Assisted Learning in a Collaborative Environment with Conventional Teaching Methods on Pre-service Teachers' Mathematics Performance," SSRN Electronic Journal, 01/01 2025, doi: 10.2139/ssrn.5141696.

[25] K. Kanagarla, "Edge computing and analytics for IoT devices: Enhancing real-time decision making in smart environments," Available at SSRN 5012466, 2024, doi: http://dx.doi.org/10.2139/ssrn.5012466.

[26] T. Nur Fitria, N. Simbolon, and Afdaleni, Internet of Things (IoT) in Education: Opportunities and Challenges. 2023.

[27] M. Chatti et al., "Learning Analytics: Challenges and Future Research Directions," eleed, vol. 10, 12/01 2014.

[28] H. A. Mutar, A. K. Abdullah, O. A. Abdaumran, I. R. N. ALRubeei, and H. T. S. ALRikabi, "Hybrid AI model-driven dynamic spectrum sharing for 6G wireless IoT networks," Studies in Informatics and Engineering, vol. 8, no. 1, 2026, doi: 10.37868/sei.v8i1.id722.

[29] S. Javed, I. Fahad, E. Azhar, M. Syed, O. Ahmed, and F. Zafar, "Bridging Learning and Technology: How Digital Platforms Impact Academic Performance," Journal for Social Science Archives, vol. 3, pp. 724-743, 02/13 2025, doi: 10.59075/jssa.v3i1.154.

[30] M. Kurni and S. K. G, "Online Laboratories Using IoT," in The Internet of Educational Things: Enhancing Students’ Engagement and Learning Performance, M. Kurni and K. G. Srinivasa Eds. Cham: Springer Nature Switzerland, 2025, pp. 97-111.

[31] H. Mutar, A. Abdullah, O. Abdaumran, I. Alrubeei, and H. Alrikabi, "Hybrid AI model-driven dynamic spectrum sharing for 6G wireless IoT networks," Sustainable Engineering and Innovation, vol. 8, pp. 53-72, 02/17 2026, doi: 10.37868/sei.v8i1.id722.

[32] R. Alfredo et al., "Human-centred learning analytics and AI in education: A systematic literature review," Computers and Education: Artificial Intelligence, vol. 6, p. 100215, 2024/06/01/ 2024, doi: https://doi.org/10.1016/j.caeai.2024.100215.

[33] F. Ouyang and L. Zhang, "AI-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review," Educational Research Review, vol. 44, p. 100616, 2024/08/01/ 2024, doi: https://doi.org/10.1016/j.edurev.2024.100616.

[34] A. Abdullah, H. Mutar, A. Hamad, I. Alrubeei, and H. Alrikabi, "Towards sustainable cropping: AI-driven precision agriculture for optimal water and pesticide use via drones and soil sensors," Heritage and Sustainable Development, vol. 8, pp. 161-176, 02/17 2026, doi: 10.37868/hsd.v8i1.1887.

[35] I. R. N. ALRubeei, H. A. Mutar, H. T. ALRikabi, I. L. H. Alsammak, H. A. Kanber, and B. H. Majeed, "Design of a Hybrid AI-Driven Engineering Model for Energy-Efficient and Sustainable Educational Systems," International Journal of Engineering Pedagogy, vol. 16, no. 1, 2026, doi: 10.3991/ijep.v16i1.60437.