An Online Personalized Learning System with Ongoing Learning Experience Adaptation: A Prototype System for STEM Discipline

Abstract

In personalized learning systems, personal information and learning information are usually analyzed for providing proper instruction for individuals. On the other hand, scholars have indicated the challenge of proposing models owing the lack of taking various data types into account. Several previous studies have indicated that without properly incorporating learning behavior into the personalized learning systems might fail to help students learn with proper learning process. To address this issue, this study proposes a model for analyzing ongoing learning data and adapting personalized learning experience. Besides, this study presents an overall structure of a personalized learning system that can work with the proposed mechanism. In comparison with other models, the proposed model performed the best with high accuracy; consequently, it can be deployed in the presented learning system. The findings of this study not only address the significance of ongoing learning experience in the personalized learning system but also enable a practical example to increase learning motivation in the online learning world, in particular STEM-related courses.