Adaptive Curriculum Development Based on Learning Analytics Analysis in Higher Education
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The integration of learning analytics into higher education has the potential to revolutionize curriculum development by providing data-driven insights into student learning patterns, strengths, and weaknesses. Adaptive curriculum development, which tailors educational content to the diverse needs of students, is becoming increasingly important as educational institutions seek to improve student engagement, retention, and success rates. However, the effective implementation of adaptive curricula based on learning analytics remains underexplored in higher education contexts. This study aims to explore the potential of learning analytics in developing adaptive curricula that align with students’ learning behaviors, preferences, and academic performance. A mixed-methods approach was employed, combining quantitative data analysis of learning analytics with qualitative feedback from students and instructors. Data was collected from a cohort of 200 students enrolled in a large university, utilizing learning management systems to track student interactions, assessments, and engagement. The results indicate that curricula developed based on learning analytics led to significant improvements in student performance and engagement, particularly for at-risk students. Personalized learning paths and real-time adjustments were shown to enhance learning outcomes. This study concludes that learning analytics can play a crucial role in adaptive curriculum development in higher education, providing a pathway for more effective and personalized learning experiences.
Abdulhasan, M. M., Mohammed, A., Nader, A. S., & Ali, A. T. H. (2024). Personalized Evaluation of Students’ Entrepreneurship Practicals Using Big Data Technology. Int. Conf. Emerg. Res. Comput. Sci., ICERCS. 2nd International Conference on Emerging Research in Computational Science, ICERCS 2024. Scopus. https://doi.org/10.1109/ICERCS63125.2024.10895587
Algayres, M., & Triantafyllou, E. (2020). Learning analytics in flipped classrooms: A scoping review. Electronic Journal of E-Learning, 18(5), 397–409. Scopus. https://doi.org/10.34190/JEL.18.5.003
Alshehri, M., Alamri, A., Cristea, A. I., & Stewart, C. D. (2021). Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs. International Journal of Artificial Intelligence in Education, 31(2), 215–233. Scopus. https://doi.org/10.1007/s40593-021-00246-2
Arnold, A. J., Keyel, J., Soysal, A., Kretser, M., Sagheb, S., & Rikakis, T. (2021). Toward an integrative professional and personal competency-based learning model for inclusive workforce development. Dalam Callaos N.C., Horne J., Sanchez B., & Savoie M. (Ed.), Int. Multi-Conf. Soc., Cybern. Informatics, IMSCI (hlm. 100–105). International Institute of Informatics and Systemics, IIIS; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118878655&partnerID=40&md5=e51516bb55cc72bcafdcfc766ca61b00
Butt, N. A., Mahmood, Z., Shakeel, K., Alfarhood, S., Safran, M., & Ashraf, I. (2023). Performance Prediction of Students in Higher Education Using Multi-Model Ensemble Approach. IEEE Access, 11, 136091–136108. Scopus. https://doi.org/10.1109/ACCESS.2023.3336987
Dhananjaya, G. M., Goudar, R. H., Govindaraja, K., Rathod, V. N., Patil, M., & Nabi, M. (2024). Intelligent Systems for Students: Enhancing Online Learning Experiences and Improving Outcomes through Personalized Education. Int. Conf. Innov. Nov. Eng. Technol., INNOVA - Proc. 2024 International Conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024 - Proceedings. Scopus. https://doi.org/10.1109/INNOVA63080.2024.10846979
Gardner, J., & Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 28(2), 127–203. Scopus. https://doi.org/10.1007/s11257-018-9203-z
Gasparetti, F., De Medio, C., Limongelli, C., Sciarrone, F., & Temperini, M. (2018). Prerequisites between learning objects: Automatic extraction based on a machine learning approach. Telematics and Informatics, 35(3), 595–610. Scopus. https://doi.org/10.1016/j.tele.2017.05.007
Ikegwu, A. C., Nweke, H. F., & Anikwe, C. V. (2024). Recent trends in computational intelligence for educational big data analysis. Iran Journal of Computer Science, 7(1), 103–129. Scopus. https://doi.org/10.1007/s42044-023-00158-5
Islam, U., Alali, I. K., Alotaibi, S. D., Alzaid, Z., Shah, B., Ali, I., & Moreira, F. (2025). Introducing the Hyperdynamic Adaptive Learning Fusion (HALF) model for superior predictive analytics in E-learning. Neural Computing and Applications. Scopus. https://doi.org/10.1007/s00521-025-11018-7
Itani, A., Brisson, L., & Garlatti, S. (2018). Understanding Learner’s Drop-Out in MOOCs. Dalam Yin H., Novais P., Camacho D., & Tallón-Ballesteros A.J. (Ed.), Lect. Notes Comput. Sci.: Vol. 11314 LNCS (hlm. 233–244). Springer Verlag; Scopus. https://doi.org/10.1007/978-3-030-03493-1_25
Karanth, D., Abu Arqub, S., & Dolce, C. (2024). The applications of digital technology in postgraduate orthodontic education. Seminars in Orthodontics, 30(4), 436–442. Scopus. https://doi.org/10.1053/j.sodo.2024.03.003
Khafizova, A. A., Galimov, A. M., Kharisova, S. R., Grebenshchikova, L. Y., Yagudina, R. I., & Smirnova, L. M. (2023). The impact of healthcare digitalization on the medical education curricula and programs: Points of convergence and divergence. Contemporary Educational Technology, 15(4). Scopus. https://doi.org/10.30935/cedtech/13768
Klimov, D., Rabovskaya, M., Kozlov, V., & Syskov, A. (2021). Timetable Model for Self Regulated Learning Based on Student Agent Level Restrictions Resolving. Proc. - Ural Symp. Biomed. Eng., Radioelectron. Inf. Technol., USBEREIT, 383–386. Scopus. https://doi.org/10.1109/USBEREIT51232.2021.9455024
Lang, D., Chen, G., Mirzaei, K., & Paepcke, A. (2020). Is faster better? A study of video playback speed. ACM Int. Conf. Proc. Ser., 260–269. Scopus. https://doi.org/10.1145/3375462.3375466
Maennel, K. (2020). Learning Analytics Perspective: Evidencing Learning from Digital Datasets in Cybersecurity Exercises. Proc. - IEEE Euro. Symp. Secur. Priv. Workshops, Euro S PW, 27–36. Scopus. https://doi.org/10.1109/EuroSPW51379.2020.00013
Mora-Salinas, R. J., Perez-Rojas, D., & De La Trinidad-Rendon, J. S. (2023). Real-Time Sensory Adaptive Learning for Engineering Students. Dalam Auer M.E., Pachatz W., & Rüütmann T. (Ed.), Lect. Notes Networks Syst.: Vol. 633 LNNS (hlm. 820–831). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-26876-2_78
Moundridou, M., Virvou, M., & Tigas, O. (2018). Providing individualized support to course authors in LMSs through instructor modelling. Int. Conf. Inf., Intell., Syst. Appl., IISA. 2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018. Scopus. https://doi.org/10.1109/IISA.2018.8633606
Mukkala, P. R., Vuyyuru, T., Ramana Murthy, B. S. N. V., Rao, A. S., & Said, N. A. (2025). Integrating ICT with Artificial Intelligence for Transformative Education. Journal of Information Systems Engineering and Management, 10, 546–555. Scopus. https://doi.org/10.52783/jisem.v10i10s.1418
Oyetade, K., Zuva, T., & Harmse, A. (2025). Integrating Industry 4.0 technologies into IT education. Cogent Education, 12(1). Scopus. https://doi.org/10.1080/2331186X.2025.2479195
Quirk, M., & Chumley, H. (2018). The adaptive medical curriculum: A model for continuous improvement. Medical Teacher, 40(8), 786–790. Scopus. https://doi.org/10.1080/0142159X.2018.1484896
Reinhold, F., Hoch, S., Schiepe-Tiska, A., Strohmaier, A. R., & Reiss, K. (2021). Motivational and Emotional Orientation, Engagement, and Achievement in Mathematics. A Case Study With One Sixth-Grade Classroom Working With an Electronic Textbook on Fractions. Frontiers in Education, 6. Scopus. https://doi.org/10.3389/feduc.2021.588472
Richardson, J., Santen, S. A., Mejicano, G. C., Fancher, T., Holmboe, E., Hogan, S. O., Marin, M., & Burk-Rafel, J. (2024). Learner Assessment and Program Evaluation: Supporting Precision Education. Hepatology, 99(4), S64–S70. Scopus. https://doi.org/10.1097/ACM.0000000000005599
Saddam, H. M. I., & Hasan, G. (2024). The Best of Way of using AI Technology in Designing Technical Education Curriculum in Meeting Future Industry Demands: Smart Way. Int. Conf. Adv. Comput. Innov. Technol. Eng. ICACITE, 1403–1406. Scopus. https://doi.org/10.1109/ICACITE60783.2024.10617225
Saltman, K. J., & Means, A. J. (2019). The wiley handbook of global educational reform. Dalam An International Handb. Of Educational Reform (hlm. 545). wiley; Scopus. https://doi.org/10.1002/9781119082316
Seeling, P., McGarry, M. P., & Johnson, M. (2023). Reveal Online Learning Clickstream Data to Provide Actionable Intelligence. Proc. Front. Educ. Conf. FIE. Proceedings - Frontiers in Education Conference, FIE. Scopus. https://doi.org/10.1109/FIE58773.2023.10343069
Singh, P., Chahal, S., Tushir, M., Rao, P. N. V. S., Kadyan, S., & Muthuperumal, S. (2024). Machine Learning for Adaptive Curriculum Development: Implementing optimized Light Gradient Boosting in Global Education. Int. Conf. Intell. Syst. Adv. Appl., ICISAA. 2024 International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024. Scopus. https://doi.org/10.1109/ICISAA62385.2024.10829171
Sposato, M. (2024). Leadership training and development in the age of artificial intelligence. Development and Learning in Organizations, 38(4), 4–7. Scopus. https://doi.org/10.1108/DLO-12-2023-0256
Stoyanova, N., Hou, J., Kopotev, M., & Yangarber, R. (2021). Integration of computer-aided language learning into formal university-level L2 instruction. Analisi Linguistica e Letteraria, 29(3), 117–126. Scopus.
Tretow-Fish, T. A. B., Khalid, M. S., & Leweke, V. A. C. (2023). Prototyping the Learning Analytics Dashboards of an Adaptive Learning Platform: Faculty Perceptions Versus Designers’ Intentions. Dalam Zaphiris P., Ioannou A., & Ioannou A. (Ed.), Lect. Notes Comput. Sci.: Vol. 14040 LNCS (hlm. 531–545). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-34411-4_36
Yalcin, A., Kaw, A., & Clark, R. (2023). On learning platform metrics as markers for student success in a course. Computer Applications in Engineering Education, 31(5), 1412–1432. Scopus. https://doi.org/10.1002/cae.22653
Zhou, Z., & Li, W. (2024). Personalized College English Learning Based on Artificial Intelligence: Algorithm-Driven Adaptive Learning Method. International Journal of High Speed Electronics and Systems. Scopus. https://doi.org/10.1142/S0129156425401020
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