An Augmented Reality Based Mobile Photography Application to Improve Learning Gain, Decrease Cognitive Load, and Achieve Better Emotional State

Abstract

Taking photos by mobile phones has become an indispensable part of people's daily life with the popularity of smartphones. However professional photography content is also needed for taking high-quality photos by mobile phones as ones taken by professional cameras. Existing learning tools for photography can't provide photographic practice while presenting theoretical content, nor give real-time photography guidance according to the environment. Mobile augmented reality (AR) technology provides a new way to solve these problems by creating a real learning situation. Therefore, an innovative AR-based mobile photography application (ARMPA) is designed and developed as a method to connect theory and practice. Based on Learning-by-Doing instructional strategy, the ARMPA can timely and synchronously display the theoretical content of photography in the form of text and images corresponding to learners' practical operation. Moreover, it can provide learners real-time interaction and guidance by using AR environment sensing technology. The effect of ARMPA on learning gain and cognitive load is assessed by traditional methods of pre/post-test and questionnaires. To objectively explore the effectiveness of ARMPA from a cognitive and emotional perspective, we measure and extract four emotional state indexes of electroencephalogram (EEG) including engagement (ENG), relaxation (MED), interest (INT) and focus (FOC). Compared to traditional two-dimension (2 D) application (APP), ARMPA improves learners’ learning gains, decreases learners’ cognitive loads. The participants in the AR group achieve better emotional states than those in the 2 D group. This study provides some empirical evidence for AR technology to boost mobile phone photography.

Acknowledgements

The authors are very grateful to the anonymous reviewers for their valuable comments and suggestions.

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

This work is supported by the Project named Research on Outdoor Experiential Learning Environment Construction Method Based on Scene Perception granted by the Humanities and Social Science project of Chinese Ministry of Education [17YJA880104]; and the Research on Automatic Segmentation and Recognition of Teaching Scene with the Characteristics of Teaching Behavior of National Natural Science Foundation of China [61977034]; and the Research on Key Technology of Intelligent Education Evaluation and Service Based on Blockchain Technology (CCNU20ZN004) financially supported by self-determined research funds of CCNU from the colleges’ basic research and operation of MOE.

Notes on contributors

Gang Zhao

Gang Zhao is a professor of Faculty Artificial Intelligence in Education at Central China Normal University, China. He gets a Ph.D. from the Huazhong University of Science and Technology, China. His research includes artificial intelligence, computer vision, and teaching analysis.

Lina Zhang

Lina Zhang is a Ph.D. candidate of Faculty Artificial Intelligence in Education at Central China Normal University, China. She is also a teacher of Dali University, China. She focus on the research of augmented reality, artificial intelligence and education.

Jie Chu

Jie Chu is a Ph.D. candidate of Faculty Artificial Intelligence in Education at Central China Normal University, China. She receives a master degree in Educational Technology from Central China Normal University, China. Her research interests focus on artificial intelligence and education.

Wenjuan Zhu

Wenjuan Zhu is a lecturer of Jing Hengyi School of Education, Hangzhou Normal University. She holds a Ph.D. from Central China Normal University, China. Her research interests include computer vision and virtual reality.

Biling Hu

Biling Hu is a Master Degree candidate of Faculty Artificial Intelligence in Education at Central China Normal University, China. She holds a Bachelor Degree from Central China Normal University, China. Her research interests include text mining and intelligent teaching analysis.

Hui He

Hui He is a Ph.D. candidate of Faculty Artificial Intelligence in Education at Central China Normal University, China. She holds a Bachelor Degree in Educational Technology from Hubei Normal University, China. Her research interest is blockchain technology.

Li Yang

Li Yang is a professor of Computer School, Hubei University of Education, China. She holds a Ph.D. in December 2012. Her research interest is educational technology.