An intelligent recommender system based on K-nearest neighbors to foster self-regulated learning and reduce cognitive load in online higher education

Higher education institutions worldwide are increasingly adopting online learning systems to improve instructional quality and offer students flexible, anytime-anywhere access to educational resources. Despite these benefits, online learning environments still face notable challenges. In this learning environment, the major challenge lies in the lack of personalized learning support. The “one-size-fits-all” approach ignores learners' diverse needs, making it difficult for students to regulate their learning effectively. Without tailored guidance, students especially low achievers are more likely to experience cognitive overload, reduced motivation, disengagement, and even dropout. In response to these issues, this study presents an interactive self-regulated learning system built on a comprehensive system architecture that integrates personalized learning path recommendations based on K-nearest Neighbors (KNN) for university students. The system consists of four main components: Content Management System (CMS), Evaluation System, Recommendation System, and User Interface application. This study evaluates the system's perceived effectiveness in the “Introduction to Database Systems” course at a university located in northern Taiwan. It further investigates the system's impact on students' learning behavior, self-regulated learning skills, learning achievement and perceived cognitive load in an online learning environment. The findings suggest that students using the interactive self-regulated learning system with personalized recommendation paths exhibited higher motivation, engagement, learning achievement and self-regulated learning skills. The system also contributed to a noticeable reduction in students'perceived cognitive load.