Leveraging interest-driven embodied practices to build quantitative literacies: A case study using motion and audio capture from dance
Leveraging interest-driven embodied practices to build quantitative literacies: A case study using motion and audio capture from dance
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利用兴趣驱动的具体实践来建立定量素养：使用舞蹈中的动作和音频捕捉的案例研究
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
W. Payne
中科院分区：
文献类型：
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作者：
Yoav Bergner;Shiri Mund;Ofer Chen;W. Payne
我们报告了一项探索性工作，旨在为高中（踢踏舞）舞者设计基于兴趣的学习体验，让他们接触数学和数据科学的概念。我们假设，通过动作和音频捕捉，生成和分析来自他们自己的舞蹈动作的数据，将（a）使学习者能够在离线体验和新的抽象概念之间形成类比，以及（b）由于数据科学与舞蹈实践的相关性和有用性而支持学习动机。基于初步访谈，了解步进器的具体需求和兴趣，我们开发了一些用于视觉和声学分析的早期原型，重点关注姿势精度、节奏和频谱特征（音色）。教师和学生对这些工具的反应证明了我们的假设的支持，即离线体现认知将有助于新知识的获取，并且数据科学的感知有用性将激励学习。开发基于兴趣和具体化的数据科学课程仍然是几个有希望的未来方向。
We report on an exploratory effort to design an interest-based learning experience for high school (step) dancers to engage with concepts in mathematics and data science. We hypothesized that generating and analyzing data from their own dance movement, through motion and audio capture, would (a) enable learners to form analogies between off-line embodied experiences and new abstract concepts and (b) support motivation to learn due to perceived relevance and usefulness of data science to dance practice. Based on initial interviews to understand the specific needs and interests of the steppers, we developed some early prototypes for visual and acoustic analysis, concentrating on pose precision, tempo, and spectral characteristics (timbre) for. Teacher and student reactions to the tools demonstrated support for our hypotheses that off-line embodied cognition would help with new knowledge acquisition and that the perceived usefulness of data science would motivate learning. Several promising future directions remain to develop an interest-based and embodied data science curriculum.
影响因子：
9.9
作者：
Manches A
通讯作者：
Manches A