QoL Guaranteed Adaptation and Personalization in E-Learning Systems

This paper investigates the problem of adaptation and personalization in e-learning systems. A new metric—QoL (Quality of Learning)—is recommended for e-learning systems to evaluate the learning process. This paper proposes the Adaptive & Personalized E-Learning System (APeLS) that provides dynamic learning content and an adaptive learning process for learners to enhance the quality of learning. According to feedback from the learner, the proposed APeLS is capable of self-adjusting and self-reorganizing the learning components and paths to adapt to each user’s learning interests, abilities, and behavior. This adaptation and reconfiguration is produced according to the user’s QoL and a dynamic referred ideal learning curve. To ensure QoL, the proposed APeLS revises each user’s learning curve to match with the referred ideal learning curve. A prototype system is implemented, and the collected results are excellent.