Graduation destination prediction method and system based on three-dimensional data of university students

Abstract

The invention discloses a graduation destination prediction method and system based on three-dimensional data of university students. The method includes the steps: acquiring the three-dimensional data of the students; cleaning and standardizing the three-dimensional data; predicting the graduation destination of the students to be predicted by the processed three-dimensional data; displaying thegraduation destination prediction results of the students to be predicted; receiving student feedback data and performing recalculation if personal employment intension is inconsistent with the prediction results. Data cleaning refers to sequentially performing missing data completion, repeated data detection and data bug correction on the three-dimensional data. A reference can be provided for career planning of the students.

Description

technical field [0001] The invention relates to a graduation destination prediction method and system based on three-dimensional data of college students. Background technique [0002] As my country's higher education is transformed from elite education to mass education, the employment situation of college graduates is becoming increasingly severe. How to effectively predict the employment of graduates is not only a concern of college students, but also more and more concerned by employers and employment guidance departments. Pay attention to. [0003] First of all, from the data level, traditional graduation destination prediction relies mostly on external data such as students’ graduation grades, awards and certificates, and lacks internal data such as professional personality tests that are closely related to graduation destinations. These flat data It is difficult to show the three-dimensional situation of students during college. [0004] Secondly, in terms of the dim...

AI Technical Summary

Problems solved by technology

[0003] First of all, from the data level, traditional graduation destination prediction relies mostly on external data such as students’ graduation grades, awards and certificates, and lacks internal data such as professional personality tests that are closely related to graduation destinations. These flat data It is difficult to show the three-dimensional situation of students during college

[0004] Secondly, in terms of the dimension of analysis, traditional graduation destination prediction methods often have relatively single analysis dimensions, lacking the combined application of horizontal analysis (same grade, same major) and longitudinal analysis (current students and former students), making it difficult to form a multi-dimensional analysis for students. , dynamic evaluation

[0005] In addition, due to the closed nature of university data, it is difficult for employment portals or platforms to obtain information about the performance of college students in school, resulting in the lack of comprehensive consideration and utilization of historical information of college students in traditional methods

Images