Research On Big Data-driven Evaluation Model And The Visualization Method For Normal University Students

In 2018,the Ministry of Education issued the "Opinions on the Implementation of Excellent Teacher Training Plan 2.0",which proposed a new goal for teacher training:the comprehensive quality of teacher students has been significantly improved,laying a solid foundation for training key teachers,excellent teachers,and educators.In the process of cultivating normal students,objective and appropriate evaluation of students'comprehensive qualities can be used to guide teachers' teaching,help students self-evaluate,and predict students' future development,which is of great significance to the training of excellent teachers.The current research on the evaluation of the comprehensive quality of normal students has the problems of insufficient comprehensive evaluation indexes,slow data update,poor operability,and inaccurate qualitative rather than quantitative evaluation results.Aiming at these problems,a big data-driven evaluation model for teachers'comprehensive quality is proposed and visualized.The main research contents of the article include:(1)Researched the theoretical basis,evaluation method and visualization method of normal students' evaluation based on big data evaluation.It detailed the formative evaluation,comprehensive evaluation and personalized evaluation based on big data,introduced the big data evaluation methods based on statistics such as PCA,AHP,factor analysis,decision tree,etc.,and the visualization technology based on portrait.(2)Summarizes the prerequisites for excellent teacher students,and puts forward the six major aspects of the evaluation of excellent teacher students:educational feelings,practical ability,innovative spirit,humanities and scientific literacy,international perspective,and teacher student quality.On this basis,the classification and refinement are carried out,and each index is constructed one by one with the students 'daily life behaviors and students' campus activity data,and a comprehensive quality evaluation model based on campus big data is constructed.(3)Developed a normal student big data portrait visualization system based on a six-element evaluation model.A hierarchical display structure based on a tree diagram is used to plan the screen division scheme and the hierarchical interactive display scheme.And taking the normal school students in Huazhong Normal University as an example,the evaluation was carried out and a portrait visualization of the evaluation results was displayed.The innovation of the paper is reflected in the following aspects:(1)In view of the shortcomings of the original static and subjective evaluation methods,a standard method for normal students based on big data was constructed,and multi-level evaluation indicators were constructed from six aspects,and the correspondence between indicators and campus life data was established.Process evaluation method of data;(2)In order to improve the real-time and authenticity of the visual display of normal students 'portraits,a data-driven visual display method of normal students' portraits was constructed to achieve linkage with process evaluation.The article builds a model for evaluating the comprehensive quality of normal students and completes the overall and detailed design of the visualization platform for the comprehensive quality of normal students,providing a complete tool for the evaluation of normal students.