Abstract
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.
Highlights
It converts quantitative evaluation to qualitative evaluation and performs judgement from multiple indexes [21]. e weighted average model is regarded as a special case to fully reflect the outstanding impact of different evaluation indicators. e specific calculation formula is as follows: y€ a1xa1 + a2xa2 + · · · + anxan1/e
A higher education management system based on data mining technology is constructed to improve the low efficiency of the higher education information systems and tackle the issue of unequal distribution of curriculum resources
We construct the MVC model 2 framework of higher education management platform which is one of the powerful frameworks used for developing huge projects with ease
Summary
Many researchers have focused on the area of education management using data mining and machine learning technologies. Gutsu et al [3] put forward the subjective attitude of college teachers towards the reform of higher education. Ey concluded that, due to the immature technology, there are technical defects in the management systems in the education sector and proposed an optimization scheme which includes data normalization method, optimizing the data clustering, enhancing the prediction accuracy, and simplifying calculations and computations. Aman et al [8] applied data mining using decision tree algorithms to the problem of predicting students’ academic performance on the basis of their educational record to know whether the student will drop out or will successfully complete the academic degree program. In literature [9, 10], different categories of online and offline learning and educational systems are available, such as Sakai, Moodle, Coursera, Google Classroom, and many others which have different features with respect to online lecture session and duration, recording, number of participants, class scheduling, and coordinating software
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