Data-Driven Adaptive Course Framework—Case Study: Impact on Success and Engagement - Ademi, Neslihan; Loshkovska, Suzana (MDPI AG, 2025)
Adaptive learning tailors learning to the specific needs and preferences of the learner. Although studies focusing on adaptive learning systems became popular decades ago, there is still a need for empirical evidence on the usability of adaptive learning in various educational environments. This study uses LMS log data to elucidate an adaptive course design explicitly developed for formal educational environments in higher education institutions. The framework utilizes learning analytics and machine learning techniques. Based on learners’ online engagement and tutors’ assessment of course activities, adaptive learning paths are presented to learners. To determine whether our system can increase learner engagement and prevent failures, learner success and engagement are measured during the learning process. The results show that the proposed adaptive course framework can increase course engagement and success. However, this potential depends on several factors, such as course organization, feedback, time constraints for activities, and the use of incentives.
Keywords: data science applications in education; architectures for educational technology systems; distance education; learning analytics