Big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and Apriori algorithm

With the development of big data technology, the field of monitoring and improving teaching quality in universities has ushered in new opportunities and challenges. Big data technology enables the capture and analysis of massive amounts of data generated during the teaching process, providing the possibility for a deeper understanding of teaching activities. However, how to extract useful information from these vast amounts of data and transform it into strategies for teaching improvement is a challenge. The research aims to propose a teaching quality monitoring and improvement method based on big data technology, which combines K-means clustering algorithm and association rule mining algorithm to improve the accuracy of teaching monitoring and the effectiveness of teaching improvement. In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. Then the association rule mining algorithm is utilized to mine the data in the teaching quality monitoring indicators with association rules on the basis of the obtained cluster analysis. Finally, on the basis of association rule mining, the study constructs the assessment model of teaching quality monitoring indicators by utilizing the fused method. The outcomes revealed that the average of data analysis accuracy and the average of recall rate of the modeling method were 93.79 % and 91.95 %, respectively. Meanwhile, the evaluation time of the modeling method in the process of teaching quality monitoring data processing was 17.3 s, and the evaluation precision was 93.15 % respectively. Additionally, the process's overall confidence and enhancement are 95.01 % and 86.73 %, respectively, and the modeling method's performance is compared to other approaches. This shown that the approach may significantly boost the precision and effectiveness of monitoring the quality of instruction, as well as offer strong backing for the enhancement of instruction in higher education institutions.