Dynamic Adjustment Mechanism of Specialty Setting in Application Oriented Universities Under the Background of Big Data

• Published in: Journal of physics. Conference series Vol. 1744; no. 4; pp. 42136 - 42142
• Main Author: Tian, Ruixia
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.02.2021
• Subjects: Application-Oriented University; Big Data; Experimental Comparison; Major Setting Adjustment; Mining Algorithm
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1744/4/042136

The specialty setting and adjustment in colleges and universities are closely related to the development of local economy and society. In order to cultivate high-quality talents, colleges and universities must base on the needs of current economic development, follow the law of professional development, set up new majors and transform old majors. However, there are many defects in the dynamic adjustment mechanism of specialty setting in traditional application-oriented colleges and universities law has been unable to adapt to mass information processing. Based on this, this paper studies the dynamic adjustment mechanism of specialty setting in application-oriented universities under the background of big data. Based on the big data mining algorithm, this paper proposes a dynamic adjustment mechanism of specialty setting in application-oriented colleges and universities, and selects two majors with the same basic situation to verify the method in this paper. In the verification process, we compare the method in this paper with the traditional method. The results show that the employment rate of graduates is 63%, the employment rate of graduates is 41%, and the rate of voluntary filling is 12%. However, among the students who adopt the traditional method, the employment rate is 81%, the employment rate of graduates is 63%, and the rate of voluntary filling is 21%. It can be seen that the dynamic adjustment mechanism of specialty setting in application-oriented colleges and universities proposed in this paper is feasible under the background of big data, and this study also provides a new reference direction for the adjustment of specialty setting in colleges and universities.