Research on Innovative Employment of College Students Based on Educational Data Mining

Abstract

Abstract: The employment destination of college graduates is an important indicator for understanding the quality of student employment and talent cultivation, with multiple and complex influencing factors. This article dynamically tracks the growth and development data of three consecutive undergraduate students from a certain university, and uses machine learning classification algorithms and prediction models to accurately predict the graduation destinations of university students. Based on this, new ideas and methods for employment work of university students are explored. Research has shown that factors such as students'college entrance examination admission scores, total scholarships received, and semester GPA play an important role in predicting their ultimate destination. Based on this, the article provides corresponding countermeasures and effective suggestions for innovative research in university work.

Key words: Big data analysis, Education data mining, College students, Behavior prediction, Employment prediction, Machine learning

CLC Number:

G647.38

WANG Yuan, CONG Di, XU Xiaomeng. Research on Innovative Employment of College Students Based on Educational Data Mining[J]. The Theory and Practice of Innovation and Enntrepreneurship, 2026, 9(3): 7-10.

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