Research On The Cause Mechanism And Intervention Strategies Of Academic Crisis Based On The University Student Data

In the education work of socialist universities with Chinese characteristics in the new era,it is necessary to deepen the reform of student evaluation and promote the all-round development including morality,intelligence,sports,beauty and labor.It has become an important prerequisite and guarantee to construct higher education quality assurance system by using big data technology and comprehensively carry out the cultivation and evaluation of college students.In recent years,with the continuous deepening of the informatization construction of universities,the big data environment of universities has brought a profound impact on students related to almost all the behavioral information of students’ learning and life.Based on the big data resource environment of universities and combined with my work practice in the Network and Information Center of universities,I tentatively focus on the three scientific issues including the identification of students’ academic crisis-related factors and crisis measurement,the identification of the causal mechanism and main causative factors of the academic crisis,the early warning of the evolution of the academic crisis and the intervention strategies from the multidisciplinary perspectives of business administration,management science and education management.A Variety of methods are used including data analysis,empirical research and qualitative analysis in this thesis.I take fully advantage of the information from the big data of students’ learning and life behaviors and relevant experiential knowledge of college counselors following the "big data + small data" framework.The main research contents and innovative work are as follows:(1)Based on the campus big data of a university in Liaoning,the data analysis and qualitative analysis are used comprehensively.After the analysis and extraction of the university student behavior data resource system,I use the grounded theory to conduct qualitative analysis on the interviews of multiple counselors.The obtained results are combined with the relevant literature reviews for constructing the academic crisis measurement index system with five dimensions: total weighted average grade,the weighted average grade of compulsory courses,number of failed grades,valid credits,and make-up exam situation.The corresponding quantitative classification and comprehensive measurement method of academic crisis are given.Usability validation statistical analysis and cleaning process are conducted for the five academic crisis measures and weighted comprehensive crisis levels.The index system of academic crisis-related elements is further constructed for university students using the corresponding fusion of multi-source big data for campus datasets.(2)Data mining and summary statistics are conducted for the correlation between the academic crisis and their related factors,including personal factors,family influence,learning status,group differentiation,and information network.On the basis of significant correlations,the theoretical hypothesis model of the causal mechanism of the academic crisis of students is constructed by further integrating the factors causing the academic crisis obtained by qualitative analysis.Based on the corresponding campus data set,regression analysis is used to carry out hypothesis testing experiments and the influence mechanism of each influencing factor is analyzed.The structural equation model of the causative mechanism of the academic crisis is built,revealing and interpreting the comprehensive causative mechanism of the academic crisis of univerisity students.In this thesis,the causes of students’ academic crisis are analyzed and verified based on the data set of a university in Liaoning province.(3)Based on the significant causal factors of academic crisis,we further consider the dynamic change characteristics of the dynamic dataset of university students’ behaviors to study the early warning model of college students’ academic crisis and intervention strategies.Firstly,a Catboost-SHAP(Categorical Boosting-SHapley Additive ex Planations)model based on categorical features is constructed,and the corresponding data analysis method for academic crisis early warning is given.Then,the K-prototype data analysis clustering method is applied and label the groups of students in academic crisis from five aspects,such as personal factors,so as to formulate targeted intervention strategies for the student groups in academic crisis.The results of the study are validated based on the campus data of a university in Liaoning province,and the analysis shows that the results of the study is able to provide a rationale for individual and group intervention strategies in academic crisis.This thesis makes full use of university big data,and presents a set of systems that integrate data mining analysis,empirical research,and qualitative analysis methods.It can effectively put the university students’ learning and life behavior data and relevant empirical knowledge and information of university counselors into integration.Moreover,a theoretical framework for research on academic crisis measurement,causative mechanism,crisis early warning,and intervention of college students with the analysis model of "big data + small data" is constructed.At the same time,based on big data,real-time academic crisis tracking,forecasting,and early warning can be used to timely warn students with academic difficulties and those with high levels of academic crisis,and remind teachers and administrators to take targeted intervention strategies promptly.It can also provide students with academic difficulties with timely planning guidance and academic course counseling,reduce the risk of delaying graduation or dropping out,improve the academic management level of colleges and universities,and achieve the goal of cultivating qualified talents.Therefore,it is of great theoretical and practical significance to actively explore the use of big data analysis to comprehensively improve the quality of education and training in universities.