Optimization of Higher Education Teaching Process Evaluation and Feedback Based on Clustering Algorithm

With the advancement of big data technology, clustering algorithms are increasingly applied in the field of education. This paper explores how clustering algorithms can be leveraged to optimize the evaluation and feedback mechanism in higher education teaching processes. Firstly, this paper describes the limits of the conventional teaching assessment, and then introduces the basic theory and its superiority in education data analysis. Secondly, it discusses how to construct an assessment model for instructional process, which includes pre-processing, selecting characteristic, grouping analyzing, and interpreting result. Moreover, this paper explores how to use the results of the group to provide instructional feedback for individualized instruction and improvement of teaching quality. At last, the validity of this approach is verified by an example, and some possible ways in the future are discussed.