A Survey on the Use of Adaptive Learning Techniques Towards Learning Personalization

Abstract

E-learning has a great impact on learners today. E-learning supports enhancing learner knowledge anytime, anywhere with lesser efforts than traditional models. In these situations, nonlinear approaches often modify teaching and learning strategies according to students' needs, and hence, automated machine-guided approaches seem useful in the name of adaptive learning. It identifies individual learner styles and provides the most suitable strategy that fits each learner as a case of personalization. Adaptive learning uses personalization for continuously improving student outcomes. Personalized learning takes place when e-learning systems use educational experience supporting desires, objectives, endowments, and curiosities of each individual learner. This work has reviewed the recent developments in the problem area of learning personalization through adaptive learning. Then the solution domain methods are compared to identify the knowledge and technology gap from their limitations. These analyses help to identify research potentials in learning technology for future works.