Development and Evaluation of a Learning Analytics-Based Personalized Learning Curriculum Model for Enhancing Student Engagement
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Published:
Apr 30, 2026
Keywords:
learning analytics, personalized learning, curriculum model, student engagement, adaptive learning
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Articles
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Abstract
Student engagement is an essential component of effective learning, yet conventional curriculum practices often provide relatively uniform learning experiences despite differences in students’ abilities, learning progress, interests, and learning needs. The study employed a Research and Development (R&D) design consisting of needs analysis, model design, expert validation, pilot implementation, evaluation, and model refinement. A quasi-experimental pretest-posttest control group design was used to examine the effectiveness of the developed model. The developed model was evaluated through expert validation, practicality assessment, and student engagement measurement using a five-point Likert-scale instrument covering behavioral, emotional, and cognitive engagement. The findings indicated that students demonstrated diverse learning profiles, confirming the need for differentiated and data-informed curriculum adaptation. Expert evaluation indicated that the model was sufficiently valid and feasible for implementation, while teacher and implementation feedback demonstrated its practical potential in digital or blended learning environments.
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How to Cite
Zaheera, A., Fehmeed, F., Wathiq, W., & Reyansh, R. (2026). Development and Evaluation of a Learning Analytics-Based Personalized Learning Curriculum Model for Enhancing Student Engagement. Journal of Education Innovation and Curriculum Development, 4(1), 13–22. Retrieved from https://journals.iarn.or.id/index.php/educur/article/view/780
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Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), 569.
Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932.
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599.
Ang, K. L.-M., Ge, F. L., & Seng, K. P. (2020). Big educational data & analytics: Survey, architecture and challenges. IEEE Access, 8, 116392–116414.
Baldoni, M., Baroglio, C., Brunkhorst, I., Henze, N., Marengo, E., & Patti, V. (2011). Constraint modeling for curriculum planning and validation. Interactive Learning Environments, 19(1), 81–123.
Bergin, T. (2018). An introduction to data analysis: Quantitative, qualitative and mixed methods.
Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068.
Buckingham Shum, S., & Crick, R. D. (2012). Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 92–101.
Collaborative, I. M., Buckwalter, K. C., Cullen, L., Hanrahan, K., Kleiber, C., McCarthy, A. M., Rakel, B., Steelman, V., Tripp‐Reimer, T., & Tucker, S. (2017). Iowa model of evidence‐based practice: Revisions and validation. Worldviews on Evidence‐Based Nursing, 14(3), 175–182.
Crone, E. A., & Dahl, R. E. (2012). Understanding adolescence as a period of social–affective engagement and goal flexibility. Nature Reviews Neuroscience, 13(9), 636–650.
De Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., & Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188.
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), 53.
Elias, T. (2011). Learning analytics. Learning, 1, 22.
Ergül, N. R., & Kargın, E. K. (2014). The effect of project based learning on students’ science success. Procedia-Social and Behavioral Sciences, 136, 537–541.
Gregory, R., Failing, L., Harstone, M., Long, G., McDaniels, T., & Ohlson, D. (2012). Structured decision making: a practical guide to environmental management choices. John Wiley & Sons.
Harrington, K., Sinfield, S., & Burns, T. (2021). Student engagement. Higher Education, 106, 1–19.
Kinzie, J., Gonyea, R., Shoup, R., & Kuh, G. D. (2008). Promoting persistence and success of underrepresented students: Lessons for teaching and learning. New Directions for Teaching & Learning, 2008(115).
Kraft, M. A., & Gilmour, A. F. (2016). Can principals promote teacher development as evaluators? A case study of principals’ views and experiences. Educational Administration Quarterly, 52(5), 711–753.
Krajcik, J., McNeill, K. L., & Reiser, B. J. (2008). Learning‐goals‐driven design model: Developing curriculum materials that align with national standards and incorporate project‐based pedagogy. Science Education, 92(1), 1–32.
Lam, S., Jimerson, S., Wong, B. P. H., Kikas, E., Shin, H., Veiga, F. H., Hatzichristou, C., Polychroni, F., Cefai, C., & Negovan, V. (2014). Understanding and measuring student engagement in school: the results of an international study from 12 countries. School Psychology Quarterly, 29(2), 213.
Liu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven personalization of student learning support in higher education. In Learning analytics: Fundaments, applications, and trends: A view of the current state of the art to enhance e-learning (pp. 143–169). Springer.
Mangaroska, K., & Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516–534.
Margolis, H., & McCabe, P. P. (2003). Self-efficacy: A key to improving the motivation of struggling learners. Preventing School Failure: Alternative Education for Children and Youth, 47(4), 162–169.
Nazaretsky, T., Bar, C., Walter, M., & Alexandron, G. (2022). Empowering teachers with AI: Co-designing a learning analytics tool for personalized instruction in the science classroom. LAK22: 12th International Learning Analytics and Knowledge Conference, 1–12.
Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49–64.
Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26(3), 2623–2656.
Reeve, J. (2012). A self-determination theory perspective on student engagement. In Handbook of research on student engagement (pp. 149–172). Springer.
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), 569.
Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932.
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599.
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