Abstract
A digital campus will generate a large amount of student-related data. How to analyze and apply these data has become the key to improving the management level of students. The analysis of student behavior data can not only assist schools in early warning of dangerous events and strengthen school safety but also can use real data to describe student behavior, thereby providing quantitative data support for scholarship and grant evaluation. This paper takes a university student as the research object, collects various data in the digital campus platform, and uses an adaptive K -means algorithm in the machine learning algorithm to cluster the data. Analyze the behavior of college students from the clustering results, so as to provide a basis for the education management and learning ability improvement of college students. Specifically, the student’s study, life, and consumption data are selected as the data to describe the student’s behavior at school. This data is input into the adaptive K -means algorithm to obtain different types of student consumption habits, living habits, and learning habits. Through the analysis results, it can be found that the problem of the group of students with low financial ability, the problem of too long online time for students, and the number of books borrowed are too low. According to the characteristics of these problems, teachers and schools are provided with targeted management suggestions. The analysis of student behavior based on machine learning technology provides a reference for the formulation of students’ school management policies and provides teachers with information on students’ personality characteristics, which is conducive to improving teachers’ teaching effects. In short, the management of the results of student behavior analysis can provide a basis for the school to formulate reasonable management policies, thereby promoting precision management and scientific decision-making.
Highlights
The establishment of a digital campus has improved the efficiency of university management and has brought great convenience to students, faculty, and staff
The test set is input to the analysis model to obtain the analysis result
After the data is preprocessed, principal component analysis (PCA) is used for feature extraction to obtain feature data and perform model training
Summary
The establishment of a digital campus has improved the efficiency of university management and has brought great convenience to students, faculty, and staff. Based on the machine learning technology, this paper conducts cluster analysis on campus all-in-one card data and analyzes the behavior of students. Reference [2] proposed a preclass student performance prediction method based on multiexample multilabel learning. Based on MOOCs learner behavior data, reference [5] established a prediction model based on clustering algorithm and neural network to mine the learning rules in the learning process. (1) Collect student consumption, life, and learning data through the campus all-in-one card system and integrate data from different institutions to form a comprehensive data set for student behavior analysis (2) An adaptive K-means clustering algorithm is used for student behavior analysis. The adaptive K value is more suitable for objective reality (3) Based on the above model, the analysis results of students’ consumption, life, and learning are obtained. The analysis results can be used to improve the management level of the school and truly teach students in accordance with their aptitude
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