Application of DBSCAN clustering algorithm in evaluating students' learning status

Poor teaching effect is a common problem in the traditional teaching process. This article uses DBSCAN clustering algorithm to classify students and develops corresponding teaching strategies according to different types of students. First, extracting the characteristics of the students as data, including career orientation, self-control index, and knowledge level. Then, using the DBSCAN algorithm to cluster the students. It has been verified that the use of DBSCAN clustering algorithm to grasp the learning status of students has certain guiding significance for teaching activities.