Predicting Early Signs of Learning Disengagement Through AI-Based Behavioral Analytics in Blended Education

M. Vithor Al Faqih; Ratu  Dwi Gustia

doi:10.58524/aidie.v2i1.158

Authors

M. Vithor Al Faqih Author
Ratu Dwi Gustia UIN Raden Intan Lampung Author

DOI:

https://doi.org/10.58524/aidie.v2i1.158

Keywords:

behavioral analytics, machine learning prediction, learning disengagement index, blended education engagement

Abstract

This study examined whether behavioral analytics derived from a Moodle-based learning management system could help identify early signs of learning disengagement in blended higher education. A quantitative predictive correlational design was used with 305 undergraduate students at Universitas Islam Negeri Raden Intan Lampung, Indonesia. Five behavioral indicators, login frequency, assignment submission timeliness, discussion participation, video completion rate, and LMS interaction breadth, were analyzed alongside scores from a 24-item Learning Disengagement Index (LDI). Multiple linear regression and four machine learning classifiers were evaluated using stratified 10-fold cross-validation. The composite behavioral model was significantly associated with LDI scores (R² = .671, p < .001), with login frequency (β = −.42) and assignment submission timeliness (β = −.38) showing the strongest standardized associations. At the Week 4 checkpoint, Random Forest showed the highest classification performance among the tested algorithms (Accuracy = .832; AUC = .912), followed by Gradient Boosting. These findings suggest that early LMS behavioral traces can provide useful decision-support signals for student support in blended courses. The results should be interpreted as context-specific predictive evidence rather than causal evidence, and local validation is recommended before institutional deployment.

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References

Adnan, M., Uddin, M. I., Khan, E., Alharithi, F. S., Amin, S. U., & Alzahrani, A. A. (2022). Earliest possible global and local interpretation of students’ performance in virtual learning environment by leveraging explainable AI. IEEE Access, 10, 129843–129864. https://doi.org/10.1109/ACCESS.2022.3227072

Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100215. https://doi.org/10.1016/j.caeai.2024.100215

Alhazbi, S., Al-ali, A., Tabassum, A., Al-Ali, A., Al-Emadi, A., Khattab, T., & Hasan, M. A. (2024). Using learning analytics to measure self-regulated learning: A systematic review of empirical studies in higher education. Journal of Computer Assisted Learning, 40(4), 1658–1674. https://doi.org/10.1111/jcal.12982

Banihashem, S. K., Farrokhnia, M., Badali, M., & Noroozi, O. (2022). The impacts of constructivist learning design and learning analytics on students’ engagement and self-regulation. Innovations in Education and Teaching International, 59(4), 442–452. https://doi.org/10.1080/14703297.2021.1890634

Bergdahl, N., & Bond, M. (2022). Negotiating (dis-)engagement in K–12 blended learning. Education and Information Technologies, 27(2), 2635–2660. https://doi.org/10.1007/s10639-021-10714-w

Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21, Article 63. https://doi.org/10.1186/s41239-024-00493-y

Bond, M., Khosravi, H., de Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, Article 4. https://doi.org/10.1186/s41239-023-00436-z

Brown, A., Basson, M., Axelsen, M., Redmond, P., & Lawrence, J. (2023). Empirical evidence to support a nudge intervention for increasing online engagement in higher education. Education Sciences, 13(2), 145. https://doi.org/10.3390/educsci13020145

Cheng, A., Pei, B., & Liu, C. (2025). Balancing act: Early, fair, and accurate identification of at-risk students. Journal of Learning Analytics, 12(3), 47–65. https://doi.org/10.18608/jla.2025.8761

Dai, W., Lin, J., Jin, F. J.-Y., Tsai, Y.-S., Srivastava, N., Le Bodic, P., Gašević, D., & Chen, G. (2025). Learning analytics for early identification of at-risk students and feedback intervention. Journal of Learning Analytics, 12(3), 102–125. https://doi.org/10.18608/jla.2025.8735

Daumiller, M., Rinas, R., & Dresel, M. (2023). Relevance of students’ goals for learning engagement and knowledge gains in an online learning course. Behavioral Sciences, 13(2), 161. https://doi.org/10.3390/bs13020161

de Vreugd, L., van Leeuwen, A., Jansen, R., & van der Schaaf, M. (2024). Learning analytics dashboard design and evaluation to support student self-regulation of study behaviour. Journal of Learning Analytics, 11(3), 249–262. https://doi.org/10.18608/jla.2024.8529

Dobashi, K., Ho, C. P., Fulford, C. P., Lin, M.-F. G., & Higa, C. (2022). Learning pattern classification using Moodle logs and the visualization of browsing processes by time-series cross-section. Computers and Education: Artificial Intelligence, 3, 100105. https://doi.org/10.1016/j.caeai.2022.100105

Doherty, C. (2023). Using web log analysis to evaluate healthcare students’ engagement behaviours with multimedia lectures on YouTube. PLoS ONE, 18(4), e0284133. https://doi.org/10.1371/journal.pone.0284133

Drugova, E., Zhuravleva, I., Zakharova, U., & Latipov, A. (2024). Learning analytics driven improvements in learning design in higher education: A systematic literature review. Journal of Computer Assisted Learning, 40(2), 510–524. https://doi.org/10.1111/jcal.12894

Er, E., Silik, S., & Cansiz, S. (2024). Uncovering engagement profiles of young learners in K–8 education through learning analytics. Journal of Learning Analytics, 11(1), 101–115. https://doi.org/10.18608/jla.2024.8133

Fazil, M., Rísquez, A., & Halpin, C. (2024). A novel deep learning model for student performance prediction using engagement data. Journal of Learning Analytics, 11(2), 23–41. https://doi.org/10.18608/jla.2024.7985

Flanagan, B., Majumdar, R., & Ogata, H. (2022). Early-warning prediction of student performance and engagement in open book assessment by reading behavior analysis. International Journal of Educational Technology in Higher Education, 19, Article 41. https://doi.org/10.1186/s41239-022-00348-4

Garbers, S., Crinklaw, A. D., Brown, A. S., & Russell, R. (2023). Increasing student engagement with course content in graduate public health education: A pilot randomized trial of behavioral nudges. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11709-5

Guo, Y., & Lee, D. (2023). Differential usage of learning management systems in chemistry courses in the time after COVID-19. Journal of Chemical Education, 100(5), 2033–2038. https://doi.org/10.1021/acs.jchemed.2c00850

Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2023). Supporting self-regulated learning with learning analytics interventions: A systematic literature review. Education and Information Technologies, 28(3), 3059–3088. https://doi.org/10.1007/s10639-022-11281-4

Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E. (2023). Learning analytics on student engagement to enhance students’ learning performance: A systematic review. Sustainability, 15(10), 7849. https://doi.org/10.3390/su15107849

Khalil, M., Slade, S., & Prinsloo, P. (2024). Learning analytics in support of inclusiveness and disabled students: A systematic review. Journal of Computing in Higher Education, 36(1), 202–219. https://doi.org/10.1007/s12528-023-09363-4

Kleimola, R., Hirsto, L., & Ruokamo, H. (2025). Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study. Education and Information Technologies, 30, 4959–4986. https://doi.org/10.1007/s10639-024-12978-4

Kohnke, L., Foung, D., & Chen, J. (2022). Using learner analytics to explore the potential contribution of multimodal formative assessment to academic success in higher education. SAGE Open, 12(2), 21582440221089957. https://doi.org/10.1177/21582440221089957

Lee, C., Luo, L., Kuhlmann, S. L., Plumley, R. D., Panter, A. T., Bernacki, M. L., Greene, J. A., & Gates, K. M. (2025). Interpretable predictive analytics for online learning: A Markov-based machine learning approach. Journal of Learning Analytics, 12(2), 259–278. https://doi.org/10.18608/jla.2025.8375

Lee, J., & Kim, D. (2025). From awareness to empowerment: Self-determination theory-informed learning analytics dashboards to enhance student engagement in asynchronous online courses. Journal of Computing in Higher Education, 37(3), 1078–1118. https://doi.org/10.1007/s12528-024-09416-2

Lee, J.-E., & Recker, M. (2022). Predicting student performance by modeling participation in asynchronous discussions in university online introductory mathematical courses. Educational Technology Research and Development, 70(6), 1993–2015. https://doi.org/10.1007/s11423-022-10153-5

Papageorgiou, E., Wong, J., Khalil, M., & Cabo, A. J. (2025). Nonlinear effort-time dynamics of student engagement in a web-based learning platform: A person-oriented transition analysis. Journal of Learning Analytics, 12(2), 237–258. https://doi.org/10.18608/jla.2025.8663

Ramaswami, G., Susnjak, T., & Mathrani, A. (2023). Effectiveness of a learning analytics dashboard for increasing student engagement levels. Journal of Learning Analytics, 10(3), 115–134. https://doi.org/10.18608/jla.2023.7935

Rodríguez, P., Villanueva, A., Dombrovskaia, L., & Valenzuela, J. P. (2023). A methodology to design, develop, and evaluate machine learning models for predicting dropout in school systems: The case of Chile. Education and Information Technologies, 28, 10103–10149. https://doi.org/10.1007/s10639-022-11515-5

Shafiq, D. A., Marjani, M., Habeeb, R. A. A., & Asirvatham, D. (2022). Student retention using educational data mining and predictive analytics: A systematic literature review. IEEE Access, 10, 72480–72503. https://doi.org/10.1109/ACCESS.2022.3188767

Talamás-Carvajal, J. A., & Ceballos, H. G. (2023). A stacking ensemble machine learning method for early identification of students at risk of dropout. Education and Information Technologies, 28(9), 12169–12189. https://doi.org/10.1007/s10639-023-11682-z

Topali, P., Ortega-Arranz, A., Rodríguez-Triana, M. J., Er, E., Khalil, M., & Akçapınar, G. (2025). Designing human-centered learning analytics and artificial intelligence in education solutions: A systematic literature review. Behaviour & Information Technology, 44(5), 1071–1098. https://doi.org/10.1080/0144929X.2024.2345295

Waheed, H., Hassan, S.-U., Nawaz, R., Aljohani, N. R., Chen, G., & Gašević, D. (2023). Early prediction of learners at risk in self-paced education: A neural network approach. Expert Systems with Applications, 213, 118868. https://doi.org/10.1016/j.eswa.2022.118868

Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9, Article 11. https://doi.org/10.1186/s40561-022-00192-z

Zerkouk, M., Mihoubi, M., Chikhaoui, B., & Wang, S. (2024). A machine learning based model for student’s dropout prediction in online training. Education and Information Technologies, 29(12), 15793–15812. https://doi.org/10.1007/s10639-024-12500-w