Investigating the effects of a programming course using flipped learning

ABSTRACT

In recent years, flipping classrooms has become a popular topic of discussion. However, few previous studies have focused on the effect of the flow experience on programming self-efficacy. To address this gap, the present researchers developed a model that included six research hypotheses. The study applied a flipped classroom model to a programming course in order to examine the relationship between self-efficacy, flow experience, and learning performance in a flipped programming course. The research model was empirically examined using a survey to collect data from 46 college students in Taiwan. Programming courses were conducted for 180 minutes once a week, with six weeks of total teaching time. The findings of this study indicate that there is a positive relationship between programming self-efficacy and enjoyment, engagement, and flow control. The study also revealed that certain important implications and factors to improve the design and delivery of such courses.

KEYWORDS:

• Self-efficacy
• flow experience
• learning performance
• flipped classroom
• programming course

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [MOST108-2511-H-415-003-MY3].

Notes on contributors

Po-Sheng Chiu

Po-Sheng Chiu is an assistant professor of the Department of E-learning Design and Management, National Chiayi University, Taiwan. He received his Ph.D. degree in the Department of Engineering Science, National Cheng Kung University, Taiwan, in 2013. His research interests include Educational Technology, Mobile Learning, e-Learning, Assistive technology, Maker Education, STEAM, AR/VR, and Distance Learning.

Hua-Xu Zhong

Hua-Xu Zhong received the M.S. degree in Department of E-learning Design and Management from National Chiayi University, Taiwan. He is a Doctoral Candidate on Engineering Science at National Cheng Kung University, Taiwan. His research interests include Internet of Things, Artificial Intelligence, Maker Education, STEAM, AR/VR and e-Learning technologies.

Chin-Feng Lai

Chin-Feng Lai is a professor at Department of Engineering Science, National Cheng Kung University and Department of Computer Science and Information Engineering, National Chung Cheng University since 2016. He has more than 140 journal paper publications and 9 papers selected to TOP 1% most cited articles by Essential Science Indicators (ESI). He is an associate editor-in-chief for Journal of Internet Technology and serves as associate editor for IET Networks. His research focuses on Internet of Things, Cloud Media Network, STEM Education, Big Data Computing.