Research on the Association Mechanism between College Students' Behavioral Characteristics and Comprehensive Quality Based on Multi-Dimensional Data Mining
Year: 2025
Type: conference-paper
Study design: Observational Study
Abstract: Educational digital transformation and the “dual excellence” construction project, in this kind of scenario, the finding out that there is a natural link between tendencies in terms of behavior and students' overall quality is one of the most significant assertions in order to develop an accurate way of handling education. Formulate a three-dimensional behavioral characteristics index system consisting of 4 consumption index indicators, 4 lifestyle index indicators, 2 learning habits index indicators and a "consciousness-ability-achievement" progressive comprehensive quality evaluation model. This study uses stratified sampling to collect data from 1141 college students, and then employs PCA, Improved Apriori, K-means++ Clustering to identify the relationship between the behavioral characteristics and general quality. The result shows that exercise frequency and library usage frequency positively effect general quality, the high savings behavior have negative correlation with low academic engagement. Cluster analysis shows three kinds of consumers: those who live frugally, those who strike a balance between frugality and extravagance, and those who spend extravagantly. These groups show clear differences in economic autonomy and consumption composition. More importantly, the current study has designed and developed a distributed student behavior analysis system, which contains a visual interaction platform. Multi dimensional behavioral portraits and accurate financial aid screening and visualization of association rules can be realized under this system. This study breaks through the restrictions of single dimensional study and creates a complete closed loop of 'data collection–modeling–system implementation.’It provides new methods and paths for colleges to realize personalized training and allocation of educational resources which is conducive to realizing the transformation of educational management experience to data intelligence.
Authors: Beisha Hua, Ziqian Liu, Xianglan Gao, Yue Yuan
Institution: Shanghai Lida University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4414387055