Research And Design Of Learning Behavior Analysis And Academic Warning System

It is pointed out on the "13th Five-Year" national education planning that the exploration of high school education carry out the credit system in an all-round way,implement flexible school system and students take courses independently.Students choose courses independently and each person take a course timetable,student management is changed from class to student's self-education,self-management and self-service.All the things increase the difficulty of students to complete their studies.Therefore,it is urgent for colleges and universities to dynamically track the students' learning status,discover abnormal learning behaviors,and timely warn them to help students complete their studies smoothly.Based on the students' consumption data and the online data,the Grid based on Clustering by Fast Search and Find of Density Peaks algorithm(GBCFSFDP)is adopted to implement the abnormal learning behavior analysis.According to students' achievement data,Internet data and consumption data,it can realize the correlation analysis of students' Internet behavior,consumer behavior and learning status by the Weighted Naive Bayes Classification Algorithm Based on Fruit Fly Optimization Algorithm(WNBC-FOA).On the basis of learning behavior analysis,this paper designed and made the early warning system come true.The main research contents are as follows:(1)Abnormal learning behavior analysis.According to the datasets with uneven distribution and multi density peaks,the GBCFSFDP algorithm with mesh generation and class merging techniques was proposed.The experimental results on the UCI standard dataset and the application show that the GBCFSFDP algorithm improves the clustering accuracy and can better identify students with abnormal learning behavior.(2)Relational analysis.In view of the deficiency of the conditional independence hypothesis of the NBC algorithm,the WNBC-FOA algorithmwas proposed by introducing the Fruit Fly Optimization Algorithm(FOA)to search the optimal weights of attributes.The experimental results on the UCI standard dataset and applications showed that the WNBC-FOA algorithm improves the accuracy of NBC algorithm and can better discover the correlation between students' Internet behavior,consumer behavior and learning status.(3)Academic early warning.According to the analysis of abnormal learning behavior and the results of association analysis,the academic early warning system was designed and implemented.The system consists of dynamic warning and static warning.(1)Dynamic early warning: based on the analysis result by the GBCFSFDP algorithm and the WNBC-FOA algorithm,it can do student learning status warning;(2)Static early warning:According to the historical performance data and the warning rules,it can do static warning.