The Predictive Role of Cognitive and Affective Factors on Behavioral Intention to Deep Learning in Technology-enhanced Learning

In the context of technology-enhanced learning (TEL), the cognitive and affective factors of students have a significant impact on their behavioral intention to deep learning. The purpose of this study was to construct and validate a predictive model of cognitive and affective factors on students’ behavioral intention to deep learning (commitment to work and deep thoughts) using a structural equation modeling (SEM), and to explore the relationship between cognition, affect, and behavioral intention of Chinese junior high school students in technology-enhanced learning environments (TELEs). It was found that: knowledge level as a cognitive factor didn’t directly predict commitment to work and deep thoughts. Perceived value and intrinsic interest as affective factors had mediating effects between knowledge level and commitment to work and deep thoughts. These findings of this study will provide insights into quality learning for Chinese junior high school students in TELEs.