文献类型：期刊文献

英文题名：A machine learning based method for constructing group profiles of university students

作者：Song, Ran[1,2];Pang, Fei[3];Jiang, Hongyun[1];Zhu, Hancan[1,2]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Zhejiang, Peoples R China;[2]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing 312000, Zhejiang, Peoples R China;[3]Shaoxing Univ, Student Affairs Dept, Shaoxing 312000, Zhejiang, Peoples R China

年份：2024

卷号：10

期号：7

外文期刊名：HELIYON

收录：SCI-EXPANDED(收录号：WOS:001224907400001)、、Scopus(收录号：2-s2.0-85189439526)、WOS

基金：Acknowledgments This work was supported by the Humanities and Social Science Fund of Ministry of Education of China (22JDSZ3107) . Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e29181.

语种：英文

外文关键词：Questionnaire survey; K-means clustering; Group profiling; Neural network; Classification prediction

外文摘要：This study facilitates university student profiling by constructing a prediction model to forecast the classification of future students participating in a survey, thereby enhancing the utility and effectiveness of the questionnaire approach. In the context of the ongoing digital transformation of campuses, higher education institutions are increasingly prioritizing student educational development. This shift aligns with the maturation of big data technology, prompting scholars to focus on profiling university student education. While earlier research in this area, particularly foreign studies, focus on extracting data from specific learning contexts and often relied on single data sources, our study addresses these limitations. We employ a comprehensive approach, incorporating questionnaire surveys to capture a diverse array of student data. Considering various university student attributes, we create a holistic profile of the student population. Furthermore, we use clustering techniques to develop a categorical prediction model. In our clustering analysis, we employ the K-means algorithm to group student survey data. The results reveal four distinct student profiles: Diligent Learners, Earnest Individuals, Discerning Achievers, and Moral Advocates. These profiles are subsequently used to label student groups. For the classification task, we leverage these labels to establish a prediction model based on the Back Propagation neural network, with the goal of assigning students to their respective groups. Through meticulous model optimization, an impressive classification accuracy of 90.22% is achieved. Our research offers a novel perspective and serves as a valuable methodological reference for university student profiling.