Digital Movement Analysis in Physical Education

Trout, Josh

doi:10.1080/07303084.2013.818394

Cited by 13 publications

(5 citation statements)

References 3 publications

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“…It is likely that the incorporation of movement analysis applications (apps) and software, such as Dartfish, UberSense, SportsCad, etc., into the assessment process could have generated a different pattern of findings. Those technological tools have indeed been shown to enhance the accuracy of assessing motor performances in a wide range of sports and physical activities (Trout, 2013).…”

Section: Discussionmentioning

confidence: 99%

Video modeling examples are effective tools for self-regulated learning in physical education: Students learn through repeated viewing, self-talk, and mental rehearsal

Trabelsi

Gharbi

Souissi

et al. 2021

European Physical Education Review

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This study aimed at assessing the impact of video modeling examples (VMEs) on secondary school students’ self-regulated learning experiences in gymnastics in original physical education (PE) settings. A total of 56 students (28 males and 28 females), aged 16–17, took part in a seven-week gymnastics learning unit for the purpose of acquiring new motor skills and knowledge using VMEs, in the absence of teacher and peer assistance. Repeated measurements were collected through gymnastics floor routines and a custom-designed written test. Semi-structured interviews were administered to explore students’ self-regulated learning strategies. The analysis of quantitative data revealed significant improvements in males’ skill performance (pre: 4.54 ± 2.42, post: 10.97 ± 3.25 points; p < 0.001) and knowledge (pre: 6.43 ± 2.26, post: 13.19 ± 3.09 points; p < 0.001), as well as females’ skill performance (pre: 2.64 ± 2.09, post: 8.96 ± 4.12 points; p < 0.001) and knowledge (pre: 6.14 ± 2.48, post: 10.97 ± 3.25 points; p < 0.001) after taking part in the practical intervention. Sex-based differences, in favor of the males, were only observed in knowledge outcomes (males: Δ (Δ%) = 6.76 (105.1%), females: Δ (Δ%) = 4.25 (69.2%); p < 0.05). Qualitative data analysis yielded three major learning strategies: repeated viewing and interpretation of VMEs, self-talk and mental rehearsal. In conclusion, VMEs were able to empower the participating students to acquire motor skills and knowledge in PE classes through activating different metacognitive learning strategies, and even without resorting to teacher/peer assistance. Data from this study could prompt PE teachers to incorporate VME-mediated self-regulated learning methods into their teaching practices for better classroom management and for improving their students’ metacognition.

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Section: Discussionmentioning

confidence: 99%

Video modeling examples are effective tools for self-regulated learning in physical education: Students learn through repeated viewing, self-talk, and mental rehearsal

Trabelsi

Gharbi

Souissi

et al. 2021

European Physical Education Review

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show abstract

“…Also, videography is used in physical education and dance education classes to analyze movement segments that are not optimally performed (cf. Trout, 2013;Li, Zhou, & Teo, 2017;Weber & Rüterboris, 2018). Therefore, in the context of teaching, digital technologies are primarily used for movement analysis and illustration (cf.…”

Section: Changing Learning Practices Through Mobile Learningmentioning

confidence: 99%

Aesthetic education revised: a contribution to mobile learning in physical education

Steinberg

Zühlke

Bindel

et al. 2019

Ger J Exerc Sport Res

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“…More recently, three modeling forms have been presented: 1) self-modeling is a form of observational learning with the distinction that the observed and the observer, object, and subject, are the same person, 2) expert-modeling where the observed and the observers are not the same person and 3) model's superposition [self vs. expert model], (Boyer et al 2009;Dowrick, 2000Dowrick, , 2012. Adashevskiy, Iermakov, Korzh, Muszkieta, Krzysztof, & Cieślicka (2014), Harvey & Gittins (2014), Trout (2013), Stoicescu & Stanescu (2012), Wilson (2008), Tofan, Vlase, Teodorescu, Burca, & Candea (2006) and Durey (1995) noted that the modeling of sports skills, through simulation software and motion analysis (i.e., Human Motion Bulider, SkillSpector and Kinovea) developed athletes' knowledge and motor performance. Bandura (1997) showed the role of modeling in enhancing skill acquisition.…”

Section: Introductionmentioning

confidence: 99%

Effect of Video Modeling Process on Teaching/Learning Hurdle Clearance Situations on Physical Education Students

Amara¹,

Mkaouer²,

Nassib³

et al. 2015

APE

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The purpose of this study was to investigate the difference between two pedagogical methods in teaching/learning hurdle clearance such as the traditional learning method which was based on verbal feedback and the new technology through motion analysis and video modeling which was based on self-modeling, expert modeling and model's superposition. Twenty-seven sports science students took part in this study. Modeling group composed of 15 students and traditional group of 12 students. Both groups participated in a 10-week hurdle clearance learning (two sessions per week) which was composed of 10 sets (5 hurdles clearance per set) in total 1000 repetitions. The findings indicated a better enhancement of the learning score in the modeling group compared to the traditional one (14.26 ± 2.05 pts vs. 11.66 ± 1.72 pts, respectively with p < 0.01). In addition, the analysis of delta-percentage revealed a high increase of technical performance in the modeling group. In conclusion, video feedback with model's superposition led to a better improvement of learning in hurdle clearance more than verbal feedback.

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Digital Movement Analysis in Physical Education

Cited by 13 publications

References 3 publications

Video modeling examples are effective tools for self-regulated learning in physical education: Students learn through repeated viewing, self-talk, and mental rehearsal

Video modeling examples are effective tools for self-regulated learning in physical education: Students learn through repeated viewing, self-talk, and mental rehearsal

Aesthetic education revised: a contribution to mobile learning in physical education

Effect of Video Modeling Process on Teaching/Learning Hurdle Clearance Situations on Physical Education Students

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