A Study on Analyzing Students' Psychological Characteristics and Learning Styles Using Clustering Algorithms

Abstract

 Learning styles are also known as learning preferences or the way learners prefer to learn. Each person has his or her own characteristics and ways of dealing with attitudes, processing and perception of problems. The study collects data by distributing questionnaires to primary and secondary school students in a city, including the collection of data related to students' psychological characteristics and students' academic performance, and preprocesses the data. Subsequently, on the basis of the traditional K-means algorithm, a data analysis method of students' psychological characteristics based on the K-means clustering algorithm improved by the genetic algorithm is proposed to explore the association between students' psychological characteristics and learning styles. The results show that the K-means clustering algorithm based on genetic algorithm significantly improves the clustering speed. According to the clustering results, the students' psychological characteristics were categorized into 4 classes, and the anxiety type had the highest percentage (47.54%), and its scores were lower. There are different learning style preferences for different students' psychological characteristics, and teachers should use learning styles as a reference to design targeted teaching activities so as to improve the teaching effect.