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A Study on Differential Influence of Students' Academic Performance on Stress Perception by Machine Learning
Tian, Danmeng1,2; Zhou, Huixia1,2; Xue, Gao3
2023
通讯作者邮箱zhou, huixia
会议名称2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering, ECICE 2023
会议录名称2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering, ECICE 2023
页码313-317
会议日期不详
会议地点不详
摘要

There are differences in the impact of students' academic performance on perceived stress, and this difference may be related to students' own and family attributes. Based on the principles of education, the impact function of academic performance on perceived stress was constructed as the dependent variable in this study. The data was collected to investigate academic performance perceived stress, self-attributes, and family attributes of students in grades 7-12 from a high school in Guangxi, China. A classification model for the impact function of academic performance on perceived stress was constructed using the decision tree method in machine learning. The experimental results showed that the F1-score of the decision tree model reached 85.6% with high accuracy. The experimental results validated the hypothesis and provided several attributes relevant to the impact of academic performance on perceived stress, as well as the portrait features of the most affected student group. The results of the decision tree model showed strong interpretability and provided a reference for teaching practice.

DOI10.1109/ECICE59523.2023.10383042
收录类别EI
引用统计
文献类型会议论文
条目标识符https://ir.psych.ac.cn/handle/311026/46883
专题中国科学院心理健康重点实验室
作者单位1.Institute of Psychology, Chinese Academy of Sciences, Cas Key Laboratory of Mental Health, Beijing; 100101, China
2.University of Chinese Academy of Sciences, Department of Psychology, Beijing; 100049, China
3.The Administrative Committee of FengdongNew City, Shaanxi, Xi'an; 710100, China
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GB/T 7714
Tian, Danmeng,Zhou, Huixia,Xue, Gao. A Study on Differential Influence of Students' Academic Performance on Stress Perception by Machine Learning[C],2023:313-317.
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