Dance courses aim to not only intensify learners’ dance skills, but also cultivate their personal dancing features, self-confidence, self-reflection ability, creative power, appreciation ability, to name just a few, in order to attain much higher level artistic performance. In the present study, an approach which integrates mobile peer assessment into flipped learning is proposed. Moreover, a 9-week experiment was conducted to explore the effects of the approach on students’ dance skills, self-efficacy, and learning satisfaction. The results indicated that the students learning with the integrated mobile peer assessment and flipped learning approach had better dance skills than those learning with the conventional flipped learning approach and traditional instruction. In terms of self-efficacy, the students learning with the conventional flipped learning approach outperformed those learning with the proposed approach and traditional instruction. In terms of learning satisfaction, the conventional flipped learning approach outperformed the traditional instruction. Discussion and suggestions are provided accordingly. It is suggested that teachers who want to incorporate flipped learning into dance courses may decide whether to combine peer assessment according to the teaching objectives.

Incorporating peer-assisted learning (PAL) into curricula to foster students’ feedback skills, reduce their reliance on teachers, and enhance their confidence in learning as well as strengthening connections among peers has been recommended by scholars; however, the ability of students, who are inexperienced peer tutors, to guide others by providing feedback based on individual peers’ needs is frequently questioned. To address this issue, this study proposed an artificial intelligence(AI)-supported PAL mode based on the perspective of self-determination theory (SDT). Additionally, an AI-facilitated PAL system was developed to serve as scaffolding to support students while executing PAL tasks. To explore the effectiveness of this learning mode, this study adopted a quasi-experimental design involving two classes of students enrolled in a physical education course at a technological university. One class of 44 students served as the experimental group adopting the AI-PAL mode, while the other class of 46 students served as the control group adopting the conventional (C-PAL) mode. The collected data were analyzed using SPSS for ANCOVA. The results indicated that the AI-PAL mode significantly enhanced students’ skills performance, learning motivation, autonomy, competence, and relatedness, compared to the C-PAL mode. Furthermore, the interview results revealed the contributions of this mode to enhancing learning effectiveness, promoting the quality of PAL, and reducing teachers’ workload. This study demonstrates the system development, application, and learning benefits of the AI-PAL mode in the physical education context. The findings could be a reference for those who intend to improve students’ skills performances using the PAL approach; moreover, it could also be a reference for developing policies to guide teachers to adopt AI in educational settings.

Background: The purpose of this study was to explore the gaze behavior of tennis players with different skill levels when receiving serves through eye movement information. Methods: The skill level was divided into group A (experts, with more than 10 years of playing experience) and group B (novices, with less than 2 years of playing experience). We compared the differences in gaze behavior between groups A and B at the head-shoulder, trunk-hips, arm-hand, leg-foot, racket, ball, and racket-ball contact area seven positions using the Eye-gaze Response Interface Computer Aid (ERICA) device. Data were analyzed using two-way ANOVA. Results: Compared with the novices, the experts have more gaze time in the head–shoulders, rack, and ball when serving forehand (p < 0.01). The experts also have more gaze time on the head–shoulders, trunk–hips, racket, ball, and racket–ball contact area when serving backhand (p < 0.05). Conclusions: Expert athletes have a longer stare time for a specific position, which mainly determines the direction of the ball. Tennis coaches can increase the gaze time for these four positions and improve tennis players’ ability to predict the direction of the ball.