基于信息技术和机器学习的学生行为

Yanfang Su
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引用次数: 0

摘要

随着科学技术的发展,人们可以从混乱的生活足迹中提取行为信息。这些行为信息可以帮助分析一个人的生活轨迹和行为规律,可以为分析一个人的生活提供便利。对学生群体进行行为分析,可以了解学生的行为习惯和特点,对提高教育管理效率具有重要的现实意义。本文主要研究基于信息技术和机器学习的学生行为(SB)分析,利用数据挖掘技术对SB信息进行挖掘,利用适当的可视化技术将挖掘结果以合适的形式显示出来,并利用机器学习技术对SB信息进行合格的显示。本文以校园一卡通和学生账户登录数据为基础,对SB进行研究和分析,对学生消费模式、生活模式、学习努力三个指标进行分析。本文研究了学生的月平均消费和消费频次,分析了学生的消费行为,并对学生出勤指数、成绩指数和课程通过率三个变量进行聚类分析,研究了学生的学习努力程度。研究结果表明,对消费者行为的分析可以使学校管理者对有需要的学生提供有针对性的帮助,并控制高消费学生。例如,第一类学生是稳定的低消费学生,占比15.45%,消费频率高但消费金额不高;2类学生为低消费不稳定人群,占7.91%,消费频率不高,且消费多为校外,消费金额不高;第三类学生是学校中占比最大的群体,作为中等消费稳定群体,其消费频次和消费金额处于平均状态;这4类学生属于中等消费群体,消费频率不稳定,消费频率不高。以校外消费为主,中间消费;5类学生为高消费稳定群体,消费金额频繁;6类学生属于高消费不稳定群体,高消费,校外消费居中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Student Behavior Based on Information Technology and Machine Learning
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.
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