Research And Application Of Big Data Technology For Student Management In Secondary Vocational Colleges

With the rapid development of the information technology,the big data technology has been put to use in all walks of life.A great deal of data is being produced every minute of the day.These data not only have innumerable values and wealth,but also challenge people's traditional perception.In recent years,the digital campus management has been very popular in the secondary vocational schools.With the large-scale expansion of the schools,the traditional means of the school management are no longer suitable for the arrival of "larger school" era.The school administrators hoped that they can use the advanced machine learning,the big data technology and statistical technology to analyze the massive data generated in the daily lives of their students in the secondary vocational college.Through the data analysis,It is hoped to find out the rules of students' daily life and their learning habits so as to provide some decision-making references for the school administrators to manage students better.The main contents of this paper were as follows.?.By studying the daily consumption data of the school students,I explored how to use the advanced machine to learn the K-means clustering algorithm and the statistical method to classify the students' consumption during the normal classes in this paper.Their average daily consumption during the attendance period was grouped into five grades such as "poverty","middle-low","medium","upper-middle" and "affluent" to make sound judgement of the students' family conditions.In this way,school jobs and financial support can be properly provided for the students whose families are relatively in trouble,so that they can complete their secondary vocational education successfully.Also,the student consumption data,their attendance data and achievement data were studied by using the multi-dimensional data clustering to explore the relationship between the students' consumption habits and their school performance.?.The correlation between the students' attendance and their examination results was also studied in this paper.By using the K-means algorithm,attempts were made to classify their attendance and the examination results.Attendance is divided into three grades: "low","normal" and "high",while the examination results are divided into five grades: "fail","pass","average","good" and "excellent".What's more,the Apriori association rule algorithm was used for the association analysis to discover the relationship between attendance and grades.In this way students could be urged to attend their classes as often as possible to improve their academic performance.In the end the school teaching management would be improved a lot by doing so.?.By studying the relevant issues concerned by the secondary vocational students in their daily life,we could classify the students' concerns into the following four kinds,that is,"satisfaction","dissatisfaction" and "concern" and "unconcern" with the means of online voting and offline research for analysis to have a better understanding of the students' daily concerns and their related needs.Attempts were also made to use the Apriori association rule algorithm to correlate the voting data.All in all,with the better understanding the needs of students and the improvement of the relevant management strategies,the school administrators can provide students with more satisfactory living and learning conditions.