ABSTRACT
In a conventional art course, it is important for a teacher to provide feedback and guidance to individual students based on their learning status. However, it is challenging for teachers to provide immediate feedback to students without any aid. The advancement of artificial intelligence (AI) has provided a possible solution to cope with this problem. In this study, a deep learning-based art learning system (DL-ALS) was developed by employing a fine-tuned ResNet50 model for helping students identify and classify artworks. We aimed at cultivating students’ accurate appreciation knowledge and artwork creation competence, as well as providing instant feedback and personalized guidance with the help of AI technology. To explore the effects of this system, a quasi-experiment was implemented in an artwork appreciation course at a university. A total of 46 university students from two classes who took the elective art course were recruited in the study. One class was the experimental group adopting DL-ALS learning, while the other was the control group adopting conventional technology-supported art learning (CT-AL). The results showed that in comparison with CT-AL, learning through the DL-ALS could facilitate students’ learning achievement, technology acceptance, learning attitude, learning motivation, self-efficacy, satisfaction, and performance in the art course.
Acknowledgements
This study is supported in part by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST 110-2511-H-167 −003-MY2.
Disclosure statement
No potential conflict of interest was reported by the author(s).
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Notes on contributors
Min-Chi Chiu
Ms. Min-Chi Chiu is a PhD at the Graduate Institute of Digital Learning and Education, National Taiwan University of Applied Science and Technology, Taiwan. Her research interests include computer-assisted learning, artificial intelligence in education, system development, mobile and ubiquitous learning.
Gwo-Jen Hwang
Dr. Gwo-Jen Hwang is a chair professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology. His research interests include mobile learning, digital game-based learning, flipped classrooms and AI in education.
Lu-Ho Hsia
Dr. Lu-Ho Hsia is an Associate Professor in the Office of Physical Education, National Chin-Yi University of Technology, Taiwan. His research interests include flipped classrooms, mobile learning and physical education.
Fong-Ming Shyu
Dr. Fong-Ming Shyu is an Associate Professor in the Department of Multimedia Design, National Taichung University of Science and Technology, Taiwan. His research interests include programming, Website system design, multimedia communication, artificial intelligence and software engineering.