Methods for Cognitive Diagnosis of Students' Abilities Based on Keystroke Features

Keystroke data contain the behavioral information of students during the programming process. The clustering analysis of keystroke data can classify students based on specific characteristics in the programming process, thereby providing a basis for personalized teaching. Research combined with keystroke features is still in its initial stage. Due to the independence and discreteness of keystroke data, and the lack of a clear requirement for the selection of the number of clusters in traditional clustering algorithms, this selection is rather arbitrary, and outliers will affect the clustering effect. Aiming at the above problems, we improve the original method. Keystroke data were used to obtain students' programming behavior information and optimize the traditional clustering algorithm according to the characteristics of keystroke data. The K-means++ algorithm was adopted to determine the initial clustering centers, the elbow method was used to determine the number of clusters, and an outlier processing algorithm was introduced. We have independently constructed a keystroke dataset for computer-based programming examinations and used it to verify our method. Moreover, the improved algorithm has shown improvements in multiple evaluation indicators. Experiments have proven that the method proposed in this paper can more accurately classify students' proficiency levels in the evaluation of students' programming abilities in the educational field. This provides strong support for the formulation of teaching strategies and the allocation of resources, and the method possesses important application value and practical significance.