Rao Wei
School of Physical Education, Wuhan University of Science and Technology, Wuhan, China.
Heliyon. 2024 Aug 15;10(16):e36393. doi: 10.1016/j.heliyon.2024.e36393. eCollection 2024 Aug 30.
This study intends to improve the efficiency of physical education teaching management, accelerate the normal teaching process, and meet the modern management requirements that traditional teaching management methods cannot meet. Based on data mining technology, this study designs a college student physical education teaching information management system, and makes a detailed design of each functional module. The main task of this study is to investigate how to effectively integrate data mining techniques with existing university student physical education teaching databases. Then, this study finds useful data information from massive data information to provide information support for university student physical education teaching. In order to effectively mine the relevant information of the data, the student evaluation module in the system is designed based on decision trees, and the teacher-student related data analysis module in the system is designed based on association rules. The research results indicate that 1039 records and 8205 student records are extracted from the teaching management database as mining objects. Rule 1: The support rate for "a professor's degree is a doctoral degree" is 20.4 %, indicating that there are 20.4 % of records in the teacher database that "the title is a professor and a doctoral degree"; the confidence level of Rule 1 is 78.2 %, indicating that 78.2 % of professors have a doctoral degree. Through the analysis of the rules that evaluate teaching as good, it can be found that the three attributes of professional title, education level, and teaching experience are the most important relevant factors affecting teaching effectiveness. Research has shown that the longer and richer the teaching experience, the stronger the teaching ability. Secondly, the mining results obtained through data mining techniques are analyzed. The maximum difference between the original algorithm's support mining results and the true values is 0.08, while the maximum difference between the improved algorithm's support mining results and the true values is 0.01. Compared to the original algorithm, the improved algorithm's mining results are accurate and effective. The application of data mining ideas in this system has laid a solid foundation for the development of physical education and teaching. Moreover, a three-layer system architecture model is adopted to better adapt to the development of school physical education, which is beneficial for later system maintenance and greatly reduces the work pressure of teachers. The system has been successfully launched and running in universities, and it is in good working condition.
本研究旨在提高体育教学管理效率，加快正常教学进程，满足传统教学管理方法无法满足的现代管理要求。基于数据挖掘技术，本研究设计了一个大学生体育教学信息管理系统，并对各功能模块进行了详细设计。本研究的主要任务是探讨如何将数据挖掘技术与现有的大学生体育教学数据库有效整合。然后，本研究从海量数据信息中找出有用的数据信息，为大学生体育教学提供信息支持。为了有效挖掘数据的相关信息，系统中的学生评价模块基于决策树进行设计，系统中的师生相关数据分析模块基于关联规则进行设计。研究结果表明，从教学管理数据库中提取了1039条记录和8205条学生记录作为挖掘对象。规则1：“教授职称是博士学位”的支持率为20.4%，表明教师数据库中有20.4%的记录“职称是教授且是博士学位”；规则1的置信度为78.2%，表明78.2%的教授拥有博士学位。通过对评价教学为优秀的规则进行分析，可以发现职称、学历和教学经验这三个属性是影响教学效果的最重要相关因素。研究表明，教学经验越长越丰富，教学能力越强。其次，对通过数据挖掘技术获得的挖掘结果进行分析。原始算法的支持度挖掘结果与真实值的最大差值为0.08，而改进算法的支持度挖掘结果与真实值的最大差值为0.01。与原始算法相比，改进算法的挖掘结果准确有效。数据挖掘思想在本系统中的应用为体育教学的发展奠定了坚实基础。此外，采用三层系统架构模型，更好地适应学校体育的发展，有利于后期系统维护，大大减轻了教师的工作压力。该系统已在高校成功上线运行，运行状况良好。
