Wanjia Li | Communication, Media and Learning Technologies Design | Mathematics Science and TechnologySkip to content Skip to main navigation
Group Model for Personalized Learning in E-learningWanjia Li, M.A. in Computing in Education
With the rapid growth of web-based tutoring system, E-learning has become a social trend. However, while using these web-based tutoring systems, students are usually overwhelmed by the great amount of information, which will cause distraction and have a negative impact on efficient learning. Being aware of this problem, many researchers have started to work on personalized learning mechanisms to improve the quality of E-learning.
This study proposes a new type of personalized learning model, Group Model, in an intelligent E-learning system to make the learning experience more effective through differentiation and group collaboration. By categorizing learners according to their individual learning characteristics, the new-type model—Group Model—puts students into different groups for group learning by clustering algorithm. The clustering algorithm from data mining is applied to develop the group model based on the student's learning characteristics data. With a comprehensive analysis, first, this study selects a reasonable and effective clustering algorithm to do cluster analysis of the model. Then, the experiment is conducted to evaluate the group model and the selected algorithm. Based on that result, the experiment shows a meaningful result and demonstrates how the group model can be successfully implemented in personalizing E-learning.