A Decision Making Approach to Evaluation of Learning Components in Adaptive Educational Systems: Proceedings of the 48th Annual Conference of the Southern African Computer Lecturers’ Association (SACLA 2019)

Abstract

Personalized learning models are developed to cater for the differentiation in learner styles and needs. Tutors determine the most appropriate learning components for each student. The learning units (LUs) are adapted to learners based on their contexts. However, there are no methods that adapt learning objects to learners based on their personalized learning styles. There also does not exist appropriate techniques that employ decision making approaches to evaluate the LUs. This study presents a model that uses learning styles to determine the appropriate learning information by employing learning analytics. Its proposed evaluation model facilitates evaluation of how suitable, acceptable and useful- ness of personalized learning in the LUs. To test the model, varying evaluation criteria weights are employed. It is proposed that the model can be used by tutors to assist learners in creating and applying LUs that are most suitable for their needs thereby improving the quality of learning.