Designing a Data-Driven, Innovative Practical Model for Minority Dance Courses in Higher Education Institutions
Abstract
This study aimed to design and evaluate a data-driven, innovative practical teaching model for minority dance courses in higher education by integrating constructivist learning theory, multicultural education, and experiential learning. The objectives were threefold: (1) to develop a systematic instructional design framework, (2) to measure students' knowledge improvement before and after applying the model, and (3) to assess student satisfaction with the model, particularly regarding cultural identity, learning experience, and engagement. A total of 17 expert instructors from Chinese universities and Kunming University were selected through purposive sampling to contribute to the design process using the Delphi Method. Additionally, 402 first-year dance students participated in evaluating the model’s effectiveness. Quantitative analysis was conducted using means, standard deviations, coefficients of variation, and t-tests. The experts' evaluation of the teaching model yielded a mean of 4.63 (SD = 0.31, CV = 17.84, p = .002), indicating moderate agreement. Student performance significantly improved after intervention, with average skill scores rising from 16.11 (SD = 0.884) to 20.33 (SD = 0.564), p = .002. Student satisfaction reached 78.58% (mean = 3.90, SD = 0.72, CV = 18.78). The hybrid teaching model—blending traditional methods with interactive digital tools and interdisciplinary content (effectively enhanced students' dance proficiency, cultural awareness, and engagement). These findings support the use of blended learning and data-informed instructional strategies to drive innovation and improve outcomes in minority dance education.
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Abstract: 89 Viewers PDF: 25 ViewersKeywords
Data-Driven Learning; Educational Data Analysis; Blended Learning Model; Minority Dance Education; Curriculum Innovation; Higher Education; Instructional Design
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DOI:
https://doi.org/10.47738/jads.v6i3.768
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Journal of Applied Data Sciences
ISSN | : | 2723-6471 (Online) |
Collaborated with | : | Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia. |
Publisher | : | Bright Publisher |
Website | : | http://bright-journal.org/JADS |
: | taqwa@amikompurwokerto.ac.id (principal contact) | |
support@bright-journal.org (technical issues) |
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