Teaching Quality Monitoring and Evaluation in Higher Education through a Big Data Analysis

Abstract

In the existing teaching quality monitoring and evaluation, there usually adopts only one method, which is relatively simple, lacking validation and verification in the analysis results. For this reason, this paper aims to conduct research on the teaching quality monitoring and evaluation in higher education based on big data analysis. Firstly, the teaching quality monitoring in higher education was made in five directions: teachers' teaching level, students' academic status, course learning effectiveness, students' competency, and students' employment status. Also, the time series forecasting model (Autoregressive Integrated Moving Average) and the differential equation model (GM(1,1)) model which can effectively predict the change trend of the series, are fused to make the predictive evaluation of the changes in data series of the higher education teaching quality. Next, a combined analysis was performed for both the teaching quality monitoring and evaluation results in higher education and the corresponding data on the frequency of proposing and promoting the improvement measures of teaching quality, and mathematical models were established through curve fitting and parameter estimation to explore the deep correlation between the two. Finally, the related experimental results were given to verify the fusion model. Therefore, the teaching quality monitoring and evaluation system in higher education based on big data analysis can realize the effective regulation of factors affecting teaching quality, and also provide convenience for the academic management of universities, which has certain research significance.