AI-facilitated reflective practice in physical education: an auto-assessment and feedback approach

ABSTRACT

To improve students’ sports skills performance, it is important to engage them in reflective practice. However, in physical classes, a teacher generally needs to face a number of students, and hence it is almost impossible to provide detailed guidance or feedback to individual students. Scholars have been trying to use Artificial Intelligence (AI) technologies to provide personalized support to individual students in diverse courses; however, in terms of promoting physical skills, there is a lack of sufficient research or practices. The present study aimed to develop a yoga automatic assessment and feedback system using AI technology to provide personalized feedback to engage individual students in reflective practice. To examine the learning effectiveness of the developed system, a total of 96 students were assigned to be the experimental group and adopted the yoga automatic assessment and feedback system for learning, while a total of 91 students were in the control group and adopted the general online learning system. The results showed that adopting the yoga automatic assessment and feedback system for learning could significantly increase students’ yoga skills performance. In addition, it had positive effects on students’ skills learning, which was conducive to “promoting reflection,” “enhancing learning motivation,” and “obtaining feedback.”

KEYWORDS:

• Physical education
• artificial intelligence
• reflective practice
• deep learning
• personalized learning

Disclosure statement

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

Additional information

Funding

This work was supported by Ministry of Science and Technology, Taiwan [grant number: MOST 110-2511-H-167 −003-MY2, MOST 111-2410-H-011 -007 -MY3].

Notes on contributors

Lu-Ho Hsia

Lu-Ho Hsia is an associate Professor of the Office of Physical Education, National Chin-Yi University of Technology, Taiwan. Her research interests include digital learning, flipped learning, mobile learning, AI in education, and physical education.

Gwo-Jen Hwang

Gwo-Jen Hwang is a chair professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology as well as an honorary chair professor of Yuan Ze University, Taiwan. His research interests include mobile learning, digital game-based learning, flipped classrooms and AI in education.

Jan-Pan Hwang

Jan-Pan Hwang is an Assistant Professor at the Dept. of Information Management at National Chin-Yi University in Taiwan. His research interests include the use of AI in education, maker education, and web-based learning. His work in the field of education aims to improve teaching and learning processes by integrating emerging technologies into educational environments.