Research on optimization strategy and practice of university course management based on machine learning and big data analysis

Abstract

Campus data is increasingly gaining the attention of college student management staff; they hope that through the use of machine learning and big data technology theory, analysis of students during the period of school data finds students growth law, timely find hidden dangers and properly handle, for students to develop more scientific and humanized management plan, truly in accordance with their individual aptitude. For Grade 13 students, the course management optimization success rates were 62.90% for Algorithm 1 and 69.35% for Algorithm 2. For grade 15 students, the success rate of course management optimization was 64.41% for algorithm 1 and 89.83% for algorithm 2. By studying the relevant techniques of student group behavior pattern analysis, we explore how to classify the behavior patterns of college students by combining machine learning K-means algorithm and NMF algorithm. Using the emotion polarity classification algorithm based on dependence, we paper how to classify the emotion polarity of student network comments and classify some real student comments in a university forum. The paper also completed the analysis of student group behavior patterns based on machine learning K-means algorithm and NMF algorithm, and obtained the classification results and characteristics of group behavior patterns, classified the real comments of some students based on the forum based on dependency, and determined the related theory based on support vector machine (SVM) algorithm. The characteristics of students’ behavior in school were analyzed from three aspects, and the K-means algorithm in the cluster analysis was used to mine the data of these three aspects, which obtained the distribution of students’ behavior characteristics of five types of consumption habits, three types of living habits and four types of learning habits. Students’ long Internet time, low number of books borrowing, and provide targeted management for teachers and schools according to the characteristics of these problems propose.