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Personalized learning is a learning approach that aims to personalize the learning experience according to the unique needs, goals, and skills of individuals which can be achieved by using current instructional technology that provides unique learning experiences in different learning environments. Technology is the main component that will enable and enrich personalized learning experience; however, even though technology is available to personalize the learning experience, there is still a lack of unified agreement on what components need to be considered for a dynamic personalized learning approach that is to be able to provide a unique and effective learning experience to each learner. To address this need, this study aims to analyze and synthesize different personalized learning approaches that consider different learning components, so that we have an evolving agreement on personalized learning models and approaches. The findings of this research identified the following main components: learner profiles and attitudes, previous knowledge and beliefs, personalized adaptive learning paths, and flexible self-paced learning environments that are generated by learning analytics. These prominent characteristics imply that a personalized learning environment (PLE) would need to be dynamic to maintain a current record of learner interests and attitudes, past experiences and performance, and activities and interactions likely to match a particular learner and learning goal.