A Study on Learning Strategies and Styles Segmentation of Learner Groups in Online Education Platforms

Abstract

Online education is becoming a popular form of education day by day. In order to provide corresponding learning programs according to the types of learning styles and learning strategies of different learners, this paper proposes a learning style recognition algorithm based on the Felder-Silverman model. The Bayesian discriminant analysis algorithm is used to calculate the probability estimation of samples belonging to one category, and then the clustering center is calculated by k-means clustering analysis so as to complete the classification of learning styles. According to the results of learning style identification, the perceptual learning group has the highest level of cooperative communication strategies in the information perception style dimension, with a mean score of 3.575, the linguistic group is the least adept at all strategies in the information input dimension, with a mean score of only 3.55, the sequential group excels in a variety of strategies for information comprehension, and the active group is the most adept at performing information processing. The personalized learning experiment was conducted according to the characteristics of different style groups. It was found that students who received personalized teaching based on their learning styles improved their performance more significantly, with their average scores in each subject increasing from 73.174 to 82.247 in the pre-test. Their scores in the post-test were 5.729 points ahead of those of the control class. The application effects are significant when learning strategies and styles are classified, and teaching programs are improved accordingly.

Keywords

• felder-silverman
• bayesian discriminant modeling
• k-means
• learning styles
• learning strategies
• 00a35