Self-Regulated Learning Path Analysis of Students with Different Grade Point Average in Flipped Classroom: K-Means Clustering and Process Mining
Keywords:
自我调节学习（SRL）, Self-regulatedlearning(SRL); flippedengineeringclassroom; processmining; K-Meansclustering, 工程翻转课堂, 过程挖掘, K均值聚类分析Abstract
Self-regulated learning (SRL) is a critical competency for students, particularly in blended learning environments like flipped classrooms. This study explores the SRL behaviors of engineering students in a flipped classroom employing a problem-based learning (PBL) approach. Taking engineering students' self-regulated learning behavior as the perspective, K-means cluster analysis was used to group students by Grade Point Average, dividing them into five categories. Then, process mining technology was applied to analyze the learning behavior characteristics of all kinds of students, aiming to provide theoretical support and practical reference based on objective data for exploring and optimizing flipped classroom teaching in engineering. It was found that there are great differences in the learning behavior paths of students with different GPA segments. Based on this, the study puts forward suggestions to improve the self-regulated learning effect of engineering students in flipped classrooms.
