Predicting digital informal learning: an empirical study among Chinese University students

Although the adoption of digital technology has gained considerable attention in higher education, currently research mainly focuses on implementation in formal learning contexts. Investigating what factors influence students’ digital informal learning is still unclear and limited. To understand better university students’ digital informal learning (DIL), this study proposed a model based on decomposed theory of planned behavior to investigate students’ behavioral intention to DIL. Different aspects of DIL behavior were further explored, through examining behaviors of cognitive learning, metacognitive learning, and social and motivation learning. This study also integrated digital competence as a new construct into the model, along with other variables to test the proposed model. A sample of 335 students selected from three universities in China took part in this study. The partial least square structural equation modeling was applied to analyze the data. The results provide support and better understanding for the importance of motivation factors such as digital competence and compatibility to explain students’ DIL.