Research on the Application of Management Decision Prediction Based on Machine Learning in Education System

Making good educational management decisions is essential for the development of the education system and the growth of students. Educational management decisions can help educational institutions better understand students’ learning and needs, so as to formulate targeted teaching plans and curriculum arrangements to improve the quality of education. Through reasonable resource allocation and teaching management, students’ learning effect and satisfaction can be improved. Educational management decisions can help educational institutions rationally allocate educational resources, including teachers, classrooms, teaching materials, etc., to meet the needs of students. Through scientific resource management, we can improve the efficiency of resource utilization and provide better educational environment and conditions. Educational management decisions can formulate personalized education plans based on students’ individual characteristics and learning needs. By understanding students’ learning ability, interests and learning styles, we can provide more suitable teaching content and methods for students and promote students’ all-round development. This paper examines the application of machine learning-based management decision prediction in the education system. With the development of education informatization, it is becoming easier to obtain and process education data, but how to use these data for decision-making and prediction has become a new problem faced by education administrators. This study proposes a machine learning-based management decision prediction method that combines data mining and machine learning techniques to extract useful information from massive amounts of education data and predict future trends and outcomes. Specifically, taking a university as an example, this paper establishes a prediction model based on decision tree algorithm by collecting and analyzing students’ academic performance and classroom performance, and predicts students’ academic performance. Experimental results show that the model can effectively predict students’ academic performance and provide valuable decision-making support for education administrators.