Adapt2Learn: A Toolkit for Configuring the Learning Algorithm for Adaptive Physical Tools for Motor-Skill Learning

Turakhia, Dishita G; Wong, Andrew; Qi, Yini; Blumberg, Lotta-Gili; Kim, Yoonji; Mueller, Stefanie

doi:10.1145/3461778.3462128

Cited by 9 publications

(1 citation statement)

References 21 publications

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“…The results in Figures 8 and 11 show that the more difficult the learner perceives the dance movements to be, the weaker the feeling that they are dancing. In addition to methods such as adjusting the opponents [18] and tools [36] according to the learners' skill level, it is advisable to present deepfake videos according to the learners' level of dance movements. For example, instead of creating a deepfake video applied to an expert's dance movements, the system generates a deepfake video that is one step closer to an expert dancer.…”

Section: Design Implicationmentioning

confidence: 99%

Dance Practice System that Shows What You Would Look Like if You Could Master the Dance

Tsuchida

Mao

Okamoto

et al. 2022

Proceedings of the 8th International Conference on Movement and Computing

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This study proposes a dance practice system allowing users to learn dancing by watching videos in which they have mastered the movements of a professional dancer. Video self-modeling, which encourages learners to improve their behavior by watching videos of exemplary behavior by themselves, effectively teaches movement skills. However, creating an ideal dance movement video is time-consuming and tedious for learners. To solve this problem, we utilize a video generation technique based on deepfake to automatically generate a video of the learners dancing the same movement as the dancer in the reference video. We conducted a user study with 20 participants to verify whether the deepfake video effectively teaches dance movements. The results showed no significant difference between the groups learning with the original and deepfake videos. In addition, the group using the deepfake video had significantly lower self-efficacy. Based on these experimental results, we discussed the design implications of the system using the deepfake video to support learning dance movements.CCS Concepts: • Human-centered computing → Interaction design; Interaction design process and methods.

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Section: Design Implicationmentioning

confidence: 99%