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Higher education has a major role to play in social progress, technology, and economic development. However, as the number of students enrolled in higher education increases, the quality of graduates is declining and their ability to work is being questioned. Therefore, it is very important to effectively evaluate the quality of teaching and learning. With this in mind, an evaluation approach using data mining based on wireless networks is proposed in this paper. In the educational work of universities, data mining helps to assist universities to improve management efficiency and teaching effectiveness in managing students by mining and analyzing large-scale educational data. In order to improve their teaching management, this paper uses the Apriori method among correlation rule mining methods. The algorithm is improved in terms of time efficiency, and then the improved algorithm is used to mine and analyze the impact of teachers' age, title, educational background, and student performance on teaching quality evaluation. The results of the exploration showed that, through the mining algorithm, it was found that the student's evaluation was proportional to the student's performance. For example, the average grade was excellent, and most evaluation grades were excellent. Its support level was 26% and its confidence level was 62.5%, indicating that more than half of the students met its characteristics. It also showed that the results were real, and the quality of teachers' teaching could be known through the results of students. [ABSTRACT FROM AUTHOR]

This paper adopts the analytical method of intelligent programming to conduct in-depth research and analysis on university education management and designs a corresponding university education management model to be applied in actual teaching. Based on the basic theories and methods of big data distributed data mining and machine learning, automated machine learning, and big data programming computational methods, this paper combines the importance and technical challenges of the algorithms themselves and the background of the practical application needs of the industry and firstly selects a series of data mining and machine learning algorithms that are commonly used based on high complexity, outstanding computational efficiency problems, and difficult to design distributed algorithms. The research on efficient large-scale distributed parallelized data mining and machine learning methods and algorithms is carried out. As an innovative teaching model, the experiential teaching model focuses on the cultivation of individual students' independent learning ability and subjective initiative, which not only can effectively activate the classroom atmosphere and improve the teaching effect but also meet the requirements of classroom teaching for the development of the times. The empirical evidence of the experiential teaching model proposed in this paper is carried out, and the implementation process of the experiential teaching model of high school physics is further improved. In terms of the overall implementation effect, the experiential teaching model proposed in the paper can deepen students' understanding of concepts and physical laws, thus enabling them to solve physics problems better, and has a positive and active effect on enhancing students' learning interests and attitudes. In the case of system security, the average comprehensive index of each unit is 61.11, 22 are higher than the average index, accounting for 61.11%, 14 are lower than the average index, accounting for 38.89%, and 23 management departments' management systems passed the safety monitoring, accounting for 63.89%. At the same time, the proposed model also provides educational practitioners with a more reliable framework for instructional design, which has realistic reference value and significance. [ABSTRACT FROM AUTHOR]

Based on data mining technology, this paper applies a combination of theoretical and practical approaches to systematically describe the background and basic concepts related to the generation of data mining-related technologies. The classical data mining process is analyzed in depth and in detail, and the method of building a decision support system for education management based on the B/S model is studied. Not only are the data mining techniques applied to this system, but also the decision tree model with the improved ID3 algorithm is implemented in this thesis, which is further applied to the educational management decision support system of this topic. The load of the client computer is reduced, and the client computer only needs to run a small part of the program. This paper focuses on the following aspects: the overall planning of the educational management decision support system based on data mining technology. From the actual educational management work, we analyze the requirements and design each functional module of this system in detail, applying the system functional structure diagram and functional use case diagram to represent the functional structure of the system and using flow charts to illustrate the workflow of the system as a whole and in parts. The logical structure design, entity-relationship design, and physical model design of the database have been carried out. To improve the efficiency of the system, the ID3 algorithm was improved on this basis to reduce the time complexity of its operation, improve the efficiency of the system operation, and achieve the goal of assessing and predicting the teaching quality of teachers. The development and design of this system provide an efficient, convenient, scientific, and reliable system tool to reduce the workload of education administrators and, more importantly, to make reasonable and effective use of the large amount of data generated in the management, and data mining techniques are used to extract valuable and potential information from these data, which can be more scientific and efficient for the teaching of teachers and students. It can provide reliable, referenceable, and valuable information for managers to make assessments and decisions. [ABSTRACT FROM AUTHOR]

In order to improve the low efficiency of higher education management and the unequal distribution of curriculum resources, facing the actual situation of higher education management, a higher education management system based on data mining technology is constructed and optimized. Using the correlation between MVC components of higher education management platform and the support of data mining technology, this paper constructs the MVC model 2 framework of higher education management platform which is a powerful framework used for developing large-scale projects with ease. We collect the data structure in the database, extract the evaluation indicators of higher education management system, separate the display results and business logic, and improve code reusability. We adopt data input, curriculum management, curriculum scheduling, teacher scheduling, and system maintenance and help to form and optimize the higher education management system. The experimental results show that the constructed and optimized higher education management system can maintain good scheduling performance and provide better teaching services for teachers and students. [ABSTRACT FROM AUTHOR]