Dancing on the Inside: A Qualitative Study on Online Dance Learning with Teacher-AI Cooperation

Autor/inn/en	Kang, Jiwon; Kang, Chaewon; Yoon, Jeewoo; Ji, Houggeun; Li, Taihu; Moon, Hyunmi; Ko, Minsam; Han, Jinyoung
Titel	Dancing on the Inside: A Qualitative Study on Online Dance Learning with Teacher-AI Cooperation
Quelle	In: Education and Information Technologies, 28 (2023) 9, S.12111-12141 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kang, Jiwon)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-023-11649-0
Schlagwörter	Educational Technology; Online Courses; Dance Education; Artificial Intelligence; Intelligent Tutoring Systems; Feedback (Response); Program Effectiveness; Teacher Role + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Online course; Online-Kurs; Dance; Education; Tanzerziehung; Künstliche Intelligenz; Intelligentes Tutorsystem; Lehrerrolle
Abstract	Recent technologies have extended opportunities for online dance learning by overcoming the limitations of space and time. However, dance teachers report that student-teacher interaction is more likely to be challenging in a distant and asynchronous learning environment than in a conventional dance class, such as a dance studio. To address this issue, we introduce DancingInside, an online dance learning system that encourages a beginner to learn dance by providing timely and sufficient feedback based on Teacher-AI cooperation. The proposed system incorporates an AI-based tutor agent (AI tutor, in short) that uses a 2D pose estimation approach to quantitatively estimate the similarity between a learner's and teacher's performance. We conducted a two-week user study with 11 students and 4 teachers. Our qualitative study results highlight that the AI tutor in DancingInside could support the reflection on a learner's practice and help the performance improvement with multimodal feedback resources. The interview results also reveal that the human teacher's role is essential in complementing the AI feedback. We discuss our design and suggest potential implications for future AI-supported cooperative dance learning systems. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01