Open AccessStudent Network Behavior Analysis and Relevance Research Based on Optimal Decision Tree Algorithms
Author(s) -
Hui Geng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1437/1/012078
Subject(s) - relevance (law) , ranking (information retrieval) , decision tree , the internet , computer science , idle , time limit , limit (mathematics) , machine learning , control (management) , tree (set theory) , artificial intelligence , mathematics education , algorithm , psychology , mathematics , engineering , world wide web , political science , law , mathematical analysis , systems engineering , operating system
Based on the optimal decision tree algorithm, this paper proposes a student network behavior analysis model. Through training and predicting the academic performance of Beihang Grade 2013 undergraduates, it is found that students’ online behavior has a profound impact on students’ academic performance. To maintain a good learning state, students must strictly limit the time spent on the Internet in idle time, effectively control the time spent on the internet, and ensure that their daily sleep is not affected by the Internet behavior. The regularity of students’ life is positively correlated with their achievement ranking.