Learning Analytics for Predicting Student Academic Success
DOI:
https://doi.org/10.37899/mjdl.v3i3.402Abstract
This study investigates the relationships among learning analytics, student engagement, and student academic success in higher education. The objective is to examine whether student engagement strengthens the influence of learning analytics on academic success. A quantitative research approach was employed using survey data collected from university students who actively utilized digital learning platforms. The data were analyzed using structural equation modeling to evaluate the direct and indirect relationships among the research variables. The findings indicate that learning analytics has a significant positive effect on student engagement and academic success, while student engagement partially mediates the relationship between learning analytics and academic success. The results suggest that learning analytics improves educational outcomes when supported by active student participation and personalized learning experiences. These findings contribute to the advancement of educational technology research and provide practical implications for universities in developing data-informed learning environments that enhance student engagement, academic achievement, and sustainable educational quality.
References
Afifuddin, M., & Seo, W. (2024). Predictive modeling for technology convergence: A patent data-driven approach through technology topic networks. Computers & Industrial Engineering, 188, 109909. https://doi.org/10.1016/j.cie.2024.109909
Alalawi, K., Athauda, R., & Chiong, R. (2025). An extended learning analytics framework integrating machine learning and pedagogical approaches for student performance prediction and intervention. International Journal of Artificial Intelligence in Education, 35(3), 1239-1287. https://doi.org/10.1007/s40593-024-00429-7
Angeioplastis, A., Aliprantis, J., Konstantakis, M., & Tsimpiris, A. (2025). Predicting student performance and enhancing learning outcomes: a data-driven approach using educational data mining techniques. Computers, 14(3), 83. https://doi.org/10.3390/computers14030083
Assefa, Y., Tilwani, S. A., Moges, B. T., Majeed, H., & Alhur, A. A. (2025). Bridging the gaps: Addressing challenges of student learning outcomes through innovative and culturally relevant approaches in pastoral areas. Social Sciences & Humanities Open, 12, 101822. https://doi.org/10.1016/j.ssaho.2025.101822
Basha, S. J., Rao, A. K., & Ammannamma, T. (2025). Transforming education with predictive analytics: A data-driven approach to student achievement. In Driving Quality Education Through AI and Data Science (pp. 433-456). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8292-9.ch019
Boonma, M. (2025). Specific Learning Disorders in Contemporary Education: A Call for Early Intervention, Inclusive Practices, and Teacher Training. Nakhon Ratchasima Journal of Humanities and Social Sciences, 1(4), 38-64.
Cabral-Gouveia, C., Menezes, I., & Neves, T. (2023, May). Educational strategies to reduce the achievement gap: a systematic review. In Frontiers in education (Vol. 8, p. 1155741). Frontiers Media SA. https://doi.org/10.3389/feduc.2023.1155741
Caspari-Sadeghi, S. (2022, January). Applying learning analytics in online environments: Measuring learners’ engagement unobtrusively. In Frontiers in Education (Vol. 7, p. 840947). Frontiers Media SA. https://doi.org/10.3389/feduc.2022.840947
Dalgaard, N. T., Bondebjerg, A., Viinholt, B. C., & Filges, T. (2022). The effects of inclusion on academic achievement, socioemotional development and wellbeing of children with special educational needs. Campbell systematic reviews, 18(4), e1291. https://doi.org/10.1002/cl2.1291
Davar, N. F., Dewan, M. A. A., & Zhang, X. (2025). AI chatbots in education: Challenges and opportunities. Information, 16(3), 235. https://doi.org/10.3390/info16030235
De Silva, L. M. H., Chounta, I. A., Rodríguez-Triana, M. J., Roa, E. R., Gramberg, A., & Valk, A. (2022). Toward an Institutional Analytics Agenda for Addressing Student Dropout in Higher Education: An Academic Stakeholders' Perspective. Journal of Learning Analytics, 9(2), 179-201. https://doi.org/10.18608/jla.2022.7507
Divjak, B., Svetec, B., Horvat, D., & Kadoić, N. (2023). Assessment validity and learning analytics as prerequisites for ensuring student‐centred learning design. British journal of educational technology, 54(1), 313-334. https://doi.org/10.1111/bjet.13290
Feraco, T., Resnati, D., Fregonese, D., Spoto, A., & Meneghetti, C. (2023). An integrated model of school students’ academic achievement and life satisfaction. Linking soft skills, extracurricular activities, self-regulated learning, motivation, and emotions: T. Feraco et al. European Journal of psychology of education, 38(1), 109-130. https://doi.org/10.1007/s10212-022-00601-4
Gonugunta, K. C., & Leo, K. (2024). Role of data-driven decision making in enhancing higher education performance: A comprehensive analysis of analytics in institutional management. International Journal of Acta Informatica, 3(1), 149-159.
Huppes, G., & Schaubroeck, T. (2022). Forecasting the future sustainability of technology choices: qualitative predictive validity of models as a complement to quantitative uncertainty. Frontiers in Sustainability, 3, 629653. https://doi.org/10.3389/frsus.2022.629653
Ifenthaler, D., Schumacher, C., & Kuzilek, J. (2023). Investigating students' use of self‐assessments in higher education using learning analytics. Journal of Computer Assisted Learning, 39(1), 255-268. https://doi.org/10.1111/jcal.12744
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
Karaoglan Yilmaz, F. G., & Yilmaz, R. (2022). Learning analytics intervention improves students’ engagement in online learning. Technology, Knowledge and Learning, 27(2), 449-460. https://doi.org/10.1007/s10758-021-09547-w
Kew, S. N., & Tasir, Z. (2022). Learning analytics in online learning environment: A systematic review on the focuses and the types of student-related analytics data. Technology, Knowledge and Learning, 27(2), 405-427. https://doi.org/10.1007/s10758-021-09541-2
Khor, E. T., & K, M. (2023). A systematic review of the role of learning analytics in supporting personalized learning. Education sciences, 14(1), 51. https://doi.org/10.3390/educsci14010051
Kim, Y. (2025). Investigating innovative teaching practices of us entrepreneurship instructors: Inquiry-based, learner-centered, and technology-integrated approaches. Entrepreneurship Education and Pedagogy, 25151274251388663. https://doi.org/10.1177/25151274251388663
Li, J., & Xue, E. (2023). Dynamic interaction between student learning behaviour and learning environment: Meta-analysis of student engagement and its influencing factors. Behavioral Sciences, 13(1), 59. https://doi.org/10.3390/bs13010059
Liu, M., & Yu, D. (2023). Towards intelligent E-learning systems. Education and Information Technologies, 28(7), 7845-7876. https://doi.org/10.1007/s10639-022-11479-6
Lynam, S., Cachia, M., & Stock, R. (2024). An evaluation of the factors that influence academic success as defined by engaged students. Educational Review, 76(3), 586-604. https://doi.org/10.1080/00131911.2022.2052808
Macnamara, B. N., & Burgoyne, A. P. (2023). Do growth mindset interventions impact students’ academic achievement? A systematic review and meta-analysis with recommendations for best practices. Psychological bulletin, 149(3-4), 133. https://doi.org/10.31234/osf.io/ba7pe
Mikić, V., Keković, G., Mangaroska, K., Ilić, M., Kopanja, L., & Vesin, B. (2026). Exploring learner engagement in e-learning environments: A predictive analytics perspective. International Journal of Human–Computer Interaction, 42(6), 3896-3919. https://doi.org/10.1080/10447318.2025.2535530
Moreeng, B. B., & Chimbunde, P. (2024). Student (Dis) Engagement in Learning amid the Economic Crisis: Challenges and Coping Strategies. Pakistan Journal of Life & Social Sciences, 22(2), 15915. https://doi.org/10.57239/PJLSS-2024-22.2.001154
Nabil, N. Z. I. N. M., Effendi, M., Matore, E. M., & Zainal, M. S. (2025). Understanding early and late identification of dyslexia: a narrative review of diagnostic timing, systemic barriers, and educational equity. e-Bangi, 22(3), 455-466. https://doi.org/10.17576/ebangi.2025.2203.36
Nguyen, Q., Rienties, B., & Whitelock, D. (2022). Informing learning design in online education using learning analytics of student engagement. Open world learning: research, innovation and the challenges of high-quality education, 189-207. https://doi.org/10.4324/9781003177098-17
Pan, Z., Biegley, L., Taylor, A., & Zheng, H. (2024). A systematic review of learning analytics: Incorporated instructional interventions on learning management systems. Journal of Learning Analytics, 11(2), 52-72. https://doi.org/10.18608/jla.2023.8093
Park, S., & Kim, N. H. (2022). University students’ self-regulation, engagement and performance in flipped learning. European Journal of Training and Development, 46(1-2), 22-40. https://doi.org/10.1108/EJTD-08-2020-0129
Ravichandran, K. (2024). Identifying learning difficulties at an early stage in education with the help of artificial intelligence models and predictive analytics. International Research Journal of Multidisciplinary Scope (IRJMS), 5(4), 1455-1461. https://doi.org/10.47857/irjms.2024.v05i04.01821
Santhiya, S., Priyanka, S., Keerthika, S., & Kumar, D. (2023, December). Early Detection and Support for Learning Disabilities: A Machine Learning Approach Empowering Educators. In 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) (pp. 1-4). IEEE. https://doi.org/10.1109/ICCEBS58601.2023.10449194
Shehata, B., Tlili, A., Huang, R., Adarkwah, M. A., Liu, M., & Chang, T. (2024). How are we doing with student-centered learning facilitated by educational technologies? A systematic review of literature reviews. Education and Information Technologies, 29(7), 7813-7854. https://doi.org/10.1007/s10639-023-12112-w
Sointu, E., Hyypiä, M., Lambert, M. C., Hirsto, L., Saarelainen, M., & Valtonen, T. (2023). Preliminary evidence of key factors in successful flipping: Predicting positive student experiences in flipped classrooms. Higher Education, 85(3), 503-520. https://doi.org/10.1007/s10734-022-00848-2
Sun, J. C. Y., Liu, Y., Lin, X., & Hu, X. (2023). Temporal learning analytics to explore traces of self-regulated learning behaviors and their associations with learning performance, cognitive load, and student engagement in an asynchronous online course. Frontiers in Psychology, 13, 1096337. https://doi.org/10.3389/fpsyg.2022.1096337
Venkatesh, V. (2022). Where to go from here? Thoughts on future directions for research on individual-level technology adoption with a focus on decision making. Venkatesh, V.'Where to go from Here, 497-518. https://doi.org/10.1111/j.1540-5414.2006.00136.x
Wang, B. (2025). The role of learning analytics in English language teaching: Investigating data-driven strategies to enhance student engagement and academic achievement. Journal of Computational Methods in Sciences and Engineering, 25(5), 4419-4436. https://doi.org/10.1177/14727978251337927
Yang, C. C., & Ogata, H. (2023). Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning. Education and Information Technologies, 28(3), 2509-2528. https://doi.org/10.1007/s10639-022-11291-2
Yeboah, G. (2026). The Impacts of Technology-Based Student-Centered Learning on Students’ Perception of Foreign Language Vocabulary Learning (French, Spanish, and Chinese) (Master's thesis, Minnesota State University, Mankato).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Mauve Journal De Leardu
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
