Prediction of Students' Use and Acceptance of Clickers by Learning Approaches: A Cross-Sectional Observational Study - Kelvin Wan; George Cheung; Kevin Chan (MDPI AG, 2017)
The student response system (a.k.a clickers) had been widely used in classrooms for various pedagogical purposes these years. However, few of the studies examine students learning approaches toward both technology and engagement. The present study adopted a cross-sectional study method to investigate the relationship between students' user acceptance of clickers, learning approaches, and general engagement in the clicker classes. A group of 3371 university students were investigated by an online questionnaire that contained with Unified Theory of Use and Acceptance of Technology, Study Process Questionnaire, and National Survey of Student Engagement across a two-semester span in 2015 and 2016. A regression analysis had been adopted to examine the relationship between those variables. Results indicated that a deep learning approach significantly predicted all user acceptance domains towards using clickers and significantly predicted several engagement domains such as collaborative learning and reflective and integrative learning. We concluded that deep learners tend to share a constructive attitude toward using clickers, especially when their peers are also using the clickers. While deep learners prefer integration of knowledge and skills from various sources and experiences, we hypothesize that their willingness to integrate clicker activities in their learning process stems from seeing clickers as a medium for consolidation in the learning process. Future research is, therefore, necessary to provide more detailed evidence of the characteristic of deep learners on the qualitative arm or in a way of mixed research method.