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Effective Modeling on Learning Ballet Online

Datos Bibliográficos

ID22044956
AutoresJeongwon Kim (0009-0003-6544-7188, Yonsei University), Iseul Jo (0000-0002-4258-9357, Yonsei University), Younha Ma (Yonsei University), Hyewon Yoon (0009-0005-5833-0701, Yonsei University), Dongwon Yook (Yonsei University), Dong-Won Yook (Yonsei University, autor de correspondencia)
Año2023
Volumen13
Número6
Páginas617
Fecha de publicación2023-06-16
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEducation Sciences (JOURNAL)
Identificadores de la revistaISSN: 2227-7102 • E-ISSN: 2227-7102
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci13060617
OpenAlexW4381252161
IdiomaEN
Referencias citadas39

After COVID-19, face-to-face learning was changed to online learning. However, very few effective online learning methods were available regarding physical education. Therefore, this study aimed to examine the modeling effects on learning ballet movement in the online system. We aimed to find effective modeling presentations based on objective information, expert assessments, and a kinematic approach. The study included 36 individuals who were divided into an expert modeling group, a self-modeling group, and controls. Participants performed 60 trials of Pas de basque in the acquisition phase and 10 trials without a demonstration video after 24 h. 10 min later, the reversed Pas de basque was conducted for the retention test. All groups showed improved performance after the acquisition phase, which indicated that the modeling presentation was effective despite adopting an online learning system. However, higher expert scores and more accurate joint movements were shown in the expert modeling group compared to the other groups. Therefore, expert modeling seems to be the most effective method for learning high-difficulty tasks with jumps and turns

Ballet · Kinematics · Multimedia · Online learning · Computer Science · Medicine · Physical Education and Pedagogy · Psychology · Sport Psychology and Performance · Sports and Physical Education Research · Artificial Intelligence

  • Historical review and appraisal of research on the learning, retention, and transfer of human motor skills

    John A Adams, Jack A Adams•Psychological Bulletin•1987

Velocidad de citaciónhistorical
Altamente citadoNo
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