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PERSONALIZED E-LEARNING MODELS: A SYSTEMATIC MAPPING STUDY

Abstract

Online learning is a tangible reality today and has a growing trend due to the rapid development of information and communication technologies in education. Personalized online learning systems are designed to tailor the learning experience to individual students' needs, preferences, and learning styles. These systems leverage a combination of advanced technologies, pedagogical strategies, and data analytics to provide customized learning paths, resources, and feedback. Personalized e-learning systems have demonstrated substantial benefits in enhancing the effectiveness, performance, and motivation of learners by tailoring educational experiences to individual needs and preferences without limiting it in space and time. This systematic mapping study aims to provide a summary of the models used to enable personalization for each e-learner including personalization components, data mining models and techniques, and interaction tools between the learner and the content of personalized e-learning. Most commonly used personalization component of personalized online learning system are learner�s profile and learning style, prior knowledge, behavior and preferences, meanwhile classification and clustering algorithms are mostly used to process these components. Through a detailed review of the literature, this study provides a structured overview of the landscape of personalized online learning, offering valuable insights into the evolution of this dynamic field and identifies key trends and patterns in the development and implementation of personalized online learning. The study also proposes directions for future research, emphasizing the importance of interdisciplinary collaboration and the continuous advancement of technology to meet the evolving needs of learners. In conclusion, personalized online learning represents a significant shift towards more individualized and effective education. This study contributes to a deeper understanding of its current state, challenges, and potential, guiding future efforts to enhance and expand personalized learning experiences for all learners

Keywords

learner model, personalized e-learning system, personalization components, data mining models, data mining techniques

Authors

MSc. Edlir SPAHO

Beder University College

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