Quality Assurance Scheme for Undergraduate Teaching Evaluation Based on Data Mining

Abstract

To solve the problems existing in the process of teaching quality evaluation and to improve the accuracy of undergraduate teaching quality evaluation, a teaching quality evaluation model based on data mining algorithm is designed. Aiming at the factors such as the large amount of data to be mined and the quality of mining which is easily affected, the improved Apriori algorithm based on partition is applied in the model. Through the process of data collection and data preprocessing, the database suitable for association rule mining is established. Then we can find out which key factors can affect the teaching quality according to the analysis of association rules, so as to provide a strong basis for teaching decision-making and management. The results show that the data mining algorithm can describe the differences between the teaching quality grades of colleges, and acquire high-precision evaluation results of university teaching quality. Moreover, the error of teaching quality evaluation in colleges is far less than that of the current typical teaching quality evaluation methods.