Application Research Of Student Behavior Mining Method Based On Campus Big Data

With the advent of the era of big data,education informatization has achieved rapid development,and the digital campus construction of various universities is steadily advancing and gradually improving.Various application systems such as the school's card system,wireless campus network,educational time and attendance system,and student management system have been built,and these application systems generate a large amount of data every day,which provides a firm foundation for educational big data mining.The role of big data in the transformation of college education has been increasingly prominent,and it has played its own unique advantages in improving the quality of school education management,teaching quality and improving the evaluation of educational achievements.In this context,this thesis takes the students of LG colleges as the research object,collects the data of various applications of LG college digital campus,and uses K-means algorithm and Apriori algorithm to conduct big data mining processing to analyze the campus behavior characteristics and learning effect of college students.The relationship between them provides empirical evidence for the improvement of educational management and learning ability of undergraduate students.Firstly,the thesis constructs the characteristics of students' school behavior from three aspects: students' learning habits,living habits and consumption habits.The characteristics of students' school behavior are analyzed from three aspects.The K-means algorithm in cluster analysis is used to mine the data of these three aspects,and five types of consumption habits,three types of habits,and four types are obtained.The distribution of students who study habits.According to the clustering results,these different types of students were analyzed in detail,and the problems of students with low economic ability,the problem that students spend too much time online,the number of books borrowed too low,etc.were found,and the teachers were based on the characteristics of these problems.And the school provides some meaningful advice.Using the student's academic curriculum scores and the results of practical innovation activities,the principal component analysis method was used to construct the students' comprehensive quality index.The Apriori association rules algorithm was used to mine the students' behavioral characteristics and the students' comprehensive quality,and the students' behavioral rules and students' comprehensiveness were obtained.The relationship between quality and the analysis of five association rules shows that the more students have good habits and study habits,the higher the overall quality of students,and vice versa.The research in this thesis provides some data support for improving the management work of the school and improving the quality of teaching work.Using the mining results to provide reference and reference for the students' school management policy formulation.The informatization teaching mode is conducive to improving the teaching results of teachers.It can also provide reasonable reference management policies for schools,and provide reference value for promoting management precision and scientific decision-making.