Research on Adaptive Tracking of University Funding Objects from the Perspective of Big Data

Abstract

It is necessary to track the funding objects of colleges and universities adaptively to understand the effect of funding for poor students. For this reason, an adaptive tracking method for university funding objects is proposed from the perspective of big data. The tracking indicators are selected through the Delphi method, and the “background”, “input”, “response”, and “output” form a pyramid tracking indicator system based on the CIRO evaluation model. The entropy weight method is used to calculate the weight of each tracking index of the system. Combining with the index membership degree, an adaptive tracking model of university funding objects is constructed to realize the analysis of funding effectiveness. The results show that the performance score of the three funded students has been increasing year by year, which indicates that the status of the three students has been significantly improved after receiving funding, but from the growth trend, the score of student 1 has entered a flat development period after getting better, which indicates that the status is stable and the funding can be gradually reduced. The score of student 2 shows a rising trend, but the rising trend is slow, so it is necessary to increase funding. Student 3 has been growing, and the score has not stabilized, so we can continue to maintain the current funding.