Predictive Learning Analytics Using Machine Learning for Cognitive Load Assessment and Knowledge Acquisition Optimization
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V8I4P107Keywords:
Machine Learning, Predictive Learning Analytics, Cognitive Load Assessment, Knowledge Acquisition, Personalized Learning, Adaptive Education, Educational Data MiningAbstract
Predictive learning analytics can support the assessment of cognitive demands and the selection of adaptive instructional pathways. Conventional assessment often emphasizes achievement and course completion without directly considering learner effort, evolving knowledge states, or the conditions under which learning occurs. This paper proposes a cognitive-aware predictive learning analytics architecture that brings together multisource learner information, feature engineering, machine-learning-based cognitive load estimation, sequential knowledge-state prediction, and adaptive instructional recommendations. The design extends the computational learning-pathway perspective of Barigidad et al. [6], which jointly considers knowledge acquisition, cognitive load management, and STEM learning performance, by specifying how predictive estimates of cognitive demands and mastery can be combined to inform instructional sequencing and support. The contribution is a proposed system architecture and a structured technical synthesis; no deployed system, empirical accuracy estimate, or demonstrated educational outcome is claimed. We identify validation requirements and explain how the integrated design could be evaluated in future studies.
References
[1] Mochocki, S. A., Reith, M. G., Borghetti, B. J., Peterson, G. L., Jasper, J. D., & Merkle, L. D. (2025). Personalized learning path problem variations: Computational complexity and AI approaches. IEEE Transactions on Artificial Intelligence, 6(3), 652–666. https://doi.org/10.1109/TAI.2024.3483190
[2] Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
[3] Baker, R. S. J. d., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
[4] Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. https://doi.org/10.1007/BF01099821
[5] Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71. https://doi.org/10.1207/S15326985EP3801_8
[6] Barigidad, S., Hameed, S., Karri, N., Jangam, S. K., Pedda, P. S. R., & Gupta, D. (2025). Computational modeling of AI-enhanced learning pathways: A mathematical framework for optimizing knowledge acquisition, cognitive load management, and student performance in STEM education. In 2025 International Conference on AI-Driven STEM Education and Learning Technologies (AISTEMEDU) (pp. 1–7). IEEE.
[7] Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438. https://doi.org/10.1007/s10648-010-9130-y
[8] Chen, S., & Epps, J. (2014). Using task-induced pupil diameter and blink rate to infer cognitive load. Human–Computer Interaction, 29(4), 390–413. https://doi.org/10.1080/07370024.2014.892428
[9] Sweller, J., Ayres, P., & Kalyuga, S. (2011). Measuring cognitive load. In Cognitive Load Theory (pp. 71–85). Springer. https://doi.org/10.1007/978-1-4419-8126-4_6
[10] Pandey, S., & Karypis, G. (2019). A self-attentive model for knowledge tracing. In Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019) (pp. 384–389). International Educational Data Mining Society.
[11] Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. In Advances in Neural Information Processing Systems (NeurIPS) (Vol. 28, pp. 505–513).
[12] Liu, Y., Yu, Y., Ye, Z., Li, M., Zhang, Y., Zhou, Z., Hu, D., & Zeng, L. (2023). Cognitive load prediction from multimodal physiological signals using multiview learning. IEEE Journal of Biomedical and Health Informatics, 28(1), 211–222. https://doi.org/10.1109/JBHI.2023.3330861
[13] Moreno-Marcos, P. M., Muñoz-Merino, P. J., Maldonado-Mahauad, J., Pérez-Sanagustín, M., Alario-Hoyos, C., & Delgado Kloos, C. (2020). Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Computers & Education, 145, 103728. https://doi.org/10.1016/j.compedu.2019.103728
[14] Liu, Q., Tong, S., Liu, C., Zhao, H., Chen, E., Ma, H., & Wang, S. (2019). Exploiting cognitive structure for adaptive learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 627–635). ACM. https://doi.org/10.1145/3292500.3330922
[15] Shen, S., Liu, Q., Huang, Z., Zheng, Y., Yin, M., Wang, M., & Chen, E. (2024). A survey of knowledge tracing: Models, variants, and applications. IEEE Transactions on Learning Technologies, 17, 1858–1879. https://doi.org/10.1109/TLT.2024.3383325
