Student Behavior Based on Information Technology and Machine Learning

Abstract

With the development of science and technology, people can extract behavioral information from the chaotic footprints of life. This behavioral information can help to analyze a person's life trajectory and behavioral rules, and can serve to analyze a person's life to facilitate this group of people. Conducting behavioral analysis on student groups can help understand students' behavioral habits, and characteristics, which has important practical significance for improving the efficiency of education management. This article mainly studies student behavior (SB) analysis based on information technology and machine learning, using data mining technology to mine SB information, using appropriate visualization techniques to display the mining results in a suitable form, and using machine learning technology to show that the SB information is qualified. This paper studies and analyzes SB, and analyzes the three indicators of student consumption pattern, life patterns, and studies effort, based on the campus all-in-one card and student account login data. This paper studies the average monthly consumption and consumption frequency of students, analyzes the consumption behavior of students, and conducts cluster analysis on the three variables of student attendance index, performance index and course pass rate to study the degree of student's study efforts. The results of the study show that the analysis of consumer behavior can enable school administrators to provide targeted assistance to students in need, and to control high-spending students. For example, type 1 students are students with stable low consumption, accounting for 15.45%, and their consumption frequency is high but the amount of consumption is not high; type 2 students are low consumption and unstable people, accounting for 7.91%, their consumption frequency is not high, and their consumption is mostly off-campus, the amount of consumption is not high; the third type of students is the group with the largest proportion in the school, as the medium consumption stable group, their consumption frequency and consumption amount are at an average state; the 4 types of students are the medium consumption group with unstable consumption frequency, and the consumption frequency is not high. Out-of-campus consumption is the main and the consumption is in the middle; students of the 5 types are high-consumption and stable groups, and the amount of consumption is frequent; the 6-type students are high-consumption and unstable groups, with high consumption and the out-of-campus consumption is in the middle.