Personalized Learning System with Hybrid Recommendation Approach

Abstract

The exponential growth of digital education has created diverse learning environments where a one-size-fits-all approach limits effectiveness. This paper presents a novel personalized learning system (PLS) designed to dynamically adapt educational content and instructional strategies based on individual learner profiles. Leveraging a hybrid recommendation approach that combines collaborative filtering and content-based techniques, the system provides tailored learning object suggestions that optimize learner engagement and knowledge retention. The proposed architecture integrates multi-dimensional learner profiling, a robust learning object repository, and an interactive user interface to facilitate seamless personalized learning experiences. Experimental evaluation involving 50 participants demonstrates the system’s efficacy, with significant improvements in learner engagement metrics, recommendation accuracy, and post-learning assessments compared to non-personalized methods. These findings substantiate the potential of adaptive learning technologies to transform education by catering to unique learner needs.