基于学生在线学习行为特征的混合课程分类研究

Classification of blended courses is crucial to the design, implementation and evaluation of the courses. At present, research on dynamic design and management, individualized assessment, and early intervention in blended learning all requires data-driven classification of the blended courses. Yet, the classification methods are still under exploration. This study extracted the data of 2456 blended courses from the learning management system of a university in fall 2018 semester, proposed a classification method of blended courses based on clustering characteristics of student online learning behaviors using this data sample, and tested the stability of the method with data of 1851 blended courses in spring 2020 semester. The result shows that (1) This method performed cluster analysis on student online learning behaviors in blended courses with machine learning algorithms, and recognized the typical pattern of students in each cluster. Accordingly, blended courses can be classified into five auto-identifiabl