Artificial Intelligence–Driven Learning Analytics for Enhancing Student Engagement and Academic Performance in Digital Learning Environments

Dendi Pratama; Eka Maya S.S. Ciptaningsih; Ramadiani Ramadiani; Achmad Fawaid; Winci Firdaus; Bambang Sudarsono

doi:10.35877/454RI.daengku4835

Authors

Dendi Pratama Universitas Media Nusantara Citra
Eka Maya S.S. Ciptaningsih Bina Nusantara University
Ramadiani Ramadiani Universitas Mulawarman
Achmad Fawaid Universitas Pembangunan Nasional "Veteran" Jawa Timur
Winci Firdaus Badan Riset dan Inovasi Nasional
Bambang Sudarsono Universitas Ahmad Dahlan

DOI:

https://doi.org/10.35877/454RI.daengku4835

Keywords:

Artificial Intelligence, Learning Analytics, Student Engagement, Academic Performance, Digital Learning Environment

Abstract

The quick development of digital learning ecosystems after educational reform in the post-pandemic era requires an increase in intelligent monitoring systems that assess student engagement and predict academic performance. Traditional learning assessment techniques frequently have flaws when detecting early disengagement signals and initiating corrective actions for at-risk students. This research proposes an Artificial Intelligence (AI)-Driven Learning Analytics method that aims to improve student engagement monitoring and academic performance prediction in digital learning environments. A fabricated LMS-based educational dataset was used, which includes behavior analysis, engagement factors, academic factors, interaction factors, and temporal learning behavior obtained from LMSs like Moodle, Google Classroom, and Canvas. Several machine learning models, including Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory (LSTM), were tested. The results revealed that the LSTM model had the best performance with an accuracy rate of 95% and a ROC-AUC value of 0.98, highlighting the importance of temporal learning behavior in educational prediction systems. Some of the essential engagement factors found to be most effective were assignment submission, quiz score, inactivity period, session length, and login number. The findings make a theoretical contribution to Artificial Intelligence in Education and Learning Analytics by combining multidimensional engagement analysis, temporal behavior modeling, and explainable AI into a unified framework. In practice, the suggested framework can aid adaptive learning, early warning, individualized intervention, and evidence-based education decisions in intelligent digital learning ecosystems.

References

Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). ChatGPT in Teaching and Learning: A Systematic Review. Education Sciences, 14(6), 643. https://doi.org/10.3390/educsci14060643

Austin, K. A. (2009). Multimedia learning: Cognitive individual differences and display design techniques predict transfer learning with multimedia learning modules. Computers and Education. https://doi.org/10.1016/j.compedu.2009.06.017

Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, 100489. https://doi.org/10.1016/j.edurev.2022.100489

Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: a systematic evidence map. International Journal of Educational Technology in Higher Education, 17(1), 2. https://doi.org/10.1186/s41239-019-0176-8

Cavus, N. (2015). Distance Learning and Learning Management Systems. Procedia - Social and Behavioral Sciences, 191, 872–877. https://doi.org/10.1016/j.sbspro.2015.04.611

Cichos, F., Gustavsson, K., Mehlig, B., & Volpe, G. (2020). Machine learning for active matter. Nature Machine Intelligence, 2(2), 94–103. https://doi.org/10.1038/s42256-020-0146-9

Cong, I., Choi, S., & Lukin, M. D. (2019). Quantum convolutional neural networks. Nature Physics, 15(12), 1273–1278. https://doi.org/10.1038/s41567-019-0648-8

Cui, Y., Chen, F., Shiri, A., & Fan, Y. (2019). Predictive analytic models of student success in higher education. Information and Learning Sciences, 120(3/4), 208–227. https://doi.org/10.1108/ILS-10-2018-0104

Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology. https://doi.org/10.1890/07-0539.1

El Aissaoui, O., El Alami El Madani, Y., Oughdir, L., Dakkak, A., & El Allioui, Y. (2020). A Multiple Linear Regression-Based Approach to Predict Student Performance. In Advances in Intelligent Systems and Computing: Vol. 1102 AISC. Springer International Publishing. https://doi.org/10.1007/978-3-030-36653-7_2

Elgazzar, M. M., & Hemayed, E. E. (2017). Electrical load forecasting using Hijri causal events. 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings. https://doi.org/10.1109/MEPCON.2016.7837003

Febriyani, R. A., Yunita, W., & Damayanti, I. (2020). An Analysis on Higher Order Thinking Skill (HOTS) in Compulsory English Textbook for the Twelfth Grade of Indonesian Senior High Schools. Journal of English Education and Teaching, 4(2), 170–183. https://doi.org/10.33369/jeet.4.2.170-183

Fitrianti, E., Annur, S., & Afriantoni. (2024). Revolusi Industri 4.0: Inovasi dan Tantangan dalam Pendidikan di Indonesia. Journal of Education and Culture, 4(1), 28–35. https://doi.org/10.58707/jec.v4i1.860

Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2022). Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research, 33(2), 678–696. https://doi.org/10.1287/isre.2021.1079

Ghosh, S., Biswas, S., Sarkar, D., & Sarkar, P. P. (2014). A novel Neuro-fuzzy classification technique for data mining. Egyptian Informatics Journal, 15(3), 129–147. https://doi.org/10.1016/j.eij.2014.08.001

Gunawan, G., Harjono, A., Suranti, N. M. Y., Herayanti, L., & Imran, I. (2021). The impact of learning management system implementation on students’ understanding of mechanics concepts. Journal of Physics: Conference Series, 1747(1), 012020. https://doi.org/10.1088/1742-6596/1747/1/012020

He, W., Xu, G., & Kruck, S. E. (2014). Online IS Education for the 21st Century. Journal of Information Systems Education, 25(2), 106.

Ingle, R., & Awale, R. N. (2018). Impact Analysis of Meditation on Physiological Signals. JOIV?: International Journal on Informatics Visualization, 2(1), 31. https://doi.org/10.30630/joiv.2.1.98

Krismadinata, Verawardina, U., Jalinus, N., Rizal, F., Sukardi, Sudira, P., Ramadhani, D., Lubis, A. L., Friadi, J., Arifin, A. S. R., & Novaliendry, D. (2020). Blended learning as instructional model in vocational education: Literature review. Universal Journal of Educational Research, 8(11B), 5801–5815. https://doi.org/10.13189/ujer.2020.082214

Larose, D. T. ., & Larose, C. D. . (2014). Discovering Knowledge in Data: An Introduction to Data Mining: Second Edition. In Discovering Knowledge in Data: An Introduction to Data Mining: Second Edition. https://doi.org/10.1002/9781118874059

Lu, Y., Yeom, S., Maktoubian, J., Rahman, M. M., & Kim, S.-H. (2025). Improve Student Risk Prediction with Clustering Techniques: A Systematic Review in Education Data Mining. Education Sciences, 15(12), 1695. https://doi.org/10.3390/educsci15121695

Mimis, M., El Hajji, M., Es-saady, Y., Oueld Guejdi, A., Douzi, H., & Mammass, D. (2019). A Framework for Smart Academic Guidance Using Educational Data Mining. Education and Information Technologies, 24(2), 1379–1393. https://doi.org/10.1007/s10639-018-9838-8

Naznin, K., Al Mahmud, A., Nguyen, M. T., & Chua, C. (2025). ChatGPT Integration in Higher Education for Personalized Learning, Academic Writing, and Coding Tasks: A Systematic Review. Computers, 14(2), 53. https://doi.org/10.3390/computers14020053

Prandner, D., Wetzelhütter, D., & Hese, S. (2025). ChatGPT as a data analyst: an exploratory study on AI-supported quantitative data analysis in empirical research. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1417900

R Vora, D., & Iyer, K. (2018). EDM – survey of performance factors and algorithms applied. International Journal of Engineering & Technology, 7(2–6), 93. https://doi.org/10.14419/ijet.v7i2.6.10074

S, V., & S, D. (2015). Data Mining Classification Algorithms for Kidney Disease Prediction. International Journal on Cybernetics & Informatics, 4(4), 13–25. https://doi.org/10.5121/ijci.2015.4402

Saptani, D. A. (2017). Teachers ’ Perception towards the Use of Quipper School in Teaching English. Advances in Social Science, Education and Humanities Research, 82(Conaplin 9), 233–235.

Self-Access Centre (SAC) in English Language Learning. (2017). Language Circle - Journal of Language and Literature. https://doi.org/10.15294/lc.v12i1.11465

Simanungkalit, A. G., & Rondonuwu, J. J. (2020). Mentoring Style, Self-Description, and Academic Achievement in English Class. Acuity: Journal of English Language Pedagogy, Literature and Culture, 5(1), 1–11. https://doi.org/10.35974/acuity.v5i1.2219

Su, X., Ning, H., Zhang, F., Liu, L., Zhang, X., & Xu, H. (2023). Application of flipped classroom based on CDIO concept combined with mini-CEX evaluation model in the clinical teaching of orthopedic nursing. BMC Medical Education, 23(1), 1–9. https://doi.org/10.1186/s12909-023-04200-9

Sudarsono, B., Saputra, W. N. E., & Ghozali, F. A. (2025). Improving student readiness for future professional activities: the Industry-Integrated Self-Design Project Learning (i-SDPL) model. The Education and Science Journal, 27(6), 29–54. https://doi.org/10.17853/1994-5639-2025-6-29-54

Triswidrananta, O. D., Pramudhita, A. N., & Wijaya, I. D. (2022). Learning Management System Based on Assessment for Learning to Improve Computational Thinking. International Journal of Interactive Mobile Technologies (IJIM), 16(04), 150–158. https://doi.org/10.3991/ijim.v16i04.28979

Vescan, A. (2019). Does Learning by Doing Have a Positive Impact on Teaching Model Checking? Proceedings of the 1st ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, 27–34. https://doi.org/10.1145/3340435.3342717

Xuan Lam, P., Mai, P. Q. H., Nguyen, Q. H., Pham, T., Nguyen, T. H. H., & Nguyen, T. H. (2024). Enhancing educational evaluation through predictive student assessment modeling. Computers and Education: Artificial Intelligence, 6, 100244. https://doi.org/10.1016/j.caeai.2024.100244

Yalçin, N., & Alisawi, M. (2024). Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques. Heliyon, 10(20). https://doi.org/10.1016/j.heliyon.2024.e38913