Enhancing the Educational Potential of Online Movement Videos: System Development and Empirical Studies with TikTok Dance Challenges

要旨

We hypothesize that online movement videos have untapped potential for teaching physical skills, and we developed a platform that automatically generates practice plans from raw TikTok dance videos. The practice plans teach one segment at a time using fading guidance and part-learning principles and are presented using a web-based interface featuring concurrent visual aids. Two user studies (n=54, n=38) were conducted. The first showed significant improvements in learning outcomes compared to standard tutorials, underscoring the importance of well-structured practice plans and offering nuanced insights into the design and effectiveness of visual aids. The second study found that segmentation and emoji-based dual-coding only benefit learning when integrated into a well-designed lesson structure. We provide a set of practical recommendations for enhancing online movement learning, focusing on the need for substantive part-learning activities and careful use of visual aids to prevent cognitive overload.

著者
Jules Brooks. Blanchet
Dartmouth College, Hanover, New Hampshire, United States
Megan E.. Hillis
Dartmouth College, Hanover, New Hampshire, United States
Yeongji Lee
Dartmouth College, Hanover, New Hampshire, United States
Qijia Shao
Hong Kong University of Science and Technology, Hong Kong, China
Xia Zhou
Columbia University , New York, New York, United States
Devin Balkcom
Dartmouth College, Hanover, New Hampshire, United States
David J. M.. Kraemer
Dartmouth College, Hanover, New Hampshire, United States
DOI

10.1145/3706598.3714062

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714062

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Innovative Learning Apporaches

G318+G319
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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