Flipped Classroom Teaching and ARCS Motivation Model: Impact on College Students’ Deep Learning

Abstract

This study examines the impact of combining Keller’s ARCS motivation theory with the flipped classroom teaching model on the deep learning of college students. Using data collected from 495 students across different regions in China, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the relationships between motivational factors and deep learning. The findings reveal that attention, relevance, confidence, and satisfaction all significantly influence deep learning. Although relevance directly enhances deep learning, its effect on intrinsic motivation is less pronounced. Furthermore, the study reveals a hierarchical relationship among the ARCS dimensions within the flipped classroom context: attention drives relevance, relevance enhances confidence, and confidence leads to satisfaction—collectively supporting a sustained learning process. These results validate the application of the ARCS model in flipped classrooms, highlighting its potential to stimulate critical thinking and improve cognitive engagement. This research contributes to the theoretical development of motivation-driven learning models. It offers practical strategies for educators to optimize instructional design, thereby fostering sustained intrinsic motivation and deep learning among students.