Collaborative Research: Integrating Physical Computing and Data Science in Movement Based Learning

协作研究：将物理计算和数据科学整合到基于运动的学习中

• 批准号: 1933961
• 负责人: Kayla DesPortes
• 金额: $ 69.47万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933961&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; Physical; Computing

This project will examine how to integrate machine learning, data science, and physical computing in the context of movement based learning. The project focuses on learning environment design for female high school students who participate in physical education activities such as dance in New York and Colorado. Many of these learners are African American and Latina, populations that are underrepresented in Science, Technology, Engineering and Math (STEM). Researchers will leverage learners' expertise and cultural practices in order to engage them in authentic and personally meaningful computing. The students will learn to create computing systems with programmable electronics worn on the body (physical computing), use those systems to create statistical models of movement and gesture (data science and machine learning), and then apply the models in a digital experiential learning environment. Researchers will work closely with physical education teachers and learners to produce design principles, curricula, new educational technologies, and comparative analyses across contexts. This project is funded by the STEM+Computing (STEM+C) program that supports research and development to understand the integration of computing and computational thinking in STEM learning.Research questions addressed in this project include: 1) How can physical education be leveraged to build expertise in computing? 2) What are the challenges of integrating computing into physical education practices? and 3) How can we meaningfully assess learning outcomes and dispositional shifts with respect to computing in the context of physical education applications? The research has three phases. Phase I will consist of conducting interviews and observations at three development sites including the non-profit organization STEM From Dance in NYC and two teams in Boulder, Colorado. Phase II will consist of participatory design sessions with physical educators and computing educators to develop a deeper understanding of how physical movement and computing (across the sub-disciplines of machine learning, data science, and physical computing) can complement one another using co-designed physical and computing learning activities. The participatory design and co-design activities will explore how sensing technologies and embodiment affordances can reshape computing education and provide alternative pathways for conceptualizing knowledge and cultivating expertise. Phase III will consist of piloting the integrated physical education and computing curricula across the three sites in NYC and Boulder. The curricula will consist of four 5-week computing modules that will be iterated on within the three development sites. Three of the modules will focus on each of the computing sub-disciplines individually, and the fourth will involve advanced topics integrating the disciplines together. This research will produce curriculum and technology to support learning modules in machine learning, data science, and, physical computing, integrating multiple levels of abstraction across the boundaries of hardware and software (i.e., cyber-physical systems). Through this collaborative inquiry researchers will develop transformative knowledge about how to embed computing into established movement based learning practices, resulting in computing curricula and tools that build on the learners' and educators' authentic practices and needs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将研究如何在基于运动的学习背景下整合机器学习、数据科学和物理计算。该项目重点关注纽约和科罗拉多州参加舞蹈等体育活动的女高中生的学习环境设计。这些学习者中有许多是非裔美国人和拉丁裔美国人，他们在科学、技术、工程和数学 (STEM) 领域的代表性不足。研究人员将利用学习者的专业知识和文化实践，让他们参与真实且对个人有意义的计算。学生将学习使用穿戴在身上的可编程电子设备创建计算系统（物理计算），使用这些系统创建运动和手势的统计模型（数据科学和机器学习），然后将这些模型应用到数字体验式学习环境中。研究人员将与体育教师和学习者密切合作，制定设计原则、课程、新的教育技术和跨环境的比较分析。该项目由 STEM+Computing (STEM+C) 计划资助，该计划支持研究和开发，以了解计算和计算思维在 STEM 学习中的整合。该项目解决的研究问题包括：1）如何利用体育教育培养计算方面的专业知识？ 2）将计算融入体育教学实践面临哪些挑战？ 3）我们如何才能有意义地评估体育应用背景下计算的学习成果和性格转变？该研究分为三个阶段。第一阶段将包括在三个开发地点进行采访和观察，包括纽约市的非营利组织 STEM From Dance 和科罗拉多州博尔德的两个团队。第二阶段将包括与体育教育工作者和计算教育工作者的参与式设计会议，以更深入地了解物理运动和计算（跨越机器学习、数据科学和物理计算的子学科）如何使用共同设计的方法相互补充物理和计算机学习活动。参与式设计和协同设计活动将探索传感技术和实施例可供性如何重塑计算教育，并为概念化知识和培养专业知识提供替代途径。第三阶段将包括在纽约市和博尔德的三个地点试行综合体育和计算机课程。该课程将包括四个为期 5 周的计算模块，这些模块将在三个开发站点内迭代。其中三个模块将分别关注每个计算子学科，第四个模块将涉及将学科整合在一起的高级主题。这项研究将开发课程和技术来支持机器学习、数据科学和物理计算的学习模块，跨硬件和软件边界（即网络物理系统）集成多个抽象层次。通过这种协作探究，研究人员将开发关于如何将计算嵌入已建立的基于运动的学习实践的变革性知识，从而产生基于学习者和教育者的真实实践和需求的计算课程和工具。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

danceON and softWEAR: Education-level creative coding and programmable wearables

danceON 和 softWEAR：教育级创意编码和可编程可穿戴设备

• DOI: 10.1145/3537972.3538015
• 发表时间: 2022
• 期刊: International Conference on Movement and Computing
• 作者: Bergner, Yoav;Payne, William;McDermott, Kathleen;Desportes, Kayla
• 通讯作者: Desportes, Kayla

“Go[ing] Hard...as a Woman of Color”: A Case Study Examining Identity Work within a Performative Dance and Computing Learning Environment

“努力……作为有色人种女性”：在表演舞蹈和计算学习环境中检验身份工作的案例研究

• DOI: 10.1145/3531000
• 发表时间: 2022
• 期刊: ACM Transactions on Computing Education
• 影响因子: 2.4
• 作者: Desportes, Kayla;McDermott, Kathleen;Bergner, Yoav;Payne, William
• 通讯作者: Payne, William

Movement Computing Education for Middle Grades

中年级运动计算教育

• DOI: 10.1145/3401956.3404238
• 发表时间: 2020
• 期刊: Proceedings of the 7th International Conference on Movement and Computing
• 作者: Bergner, Yoav;Damast, Deborah;Romita, Allegra;Smock, Anne Marie
• 通讯作者: Smock, Anne Marie

danceON: Culturally Responsive Creative Computing

danceON：文化响应式创意计算

• DOI: 10.1145/3411764.3445149
• 发表时间: 2021
• 期刊: CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
• 作者: Payne, William Christopher;Bergner, Yoav;West, Mary Etta;Charp, Carlie;Shapiro, R. Benjamin;Szafir, Danielle Albers;Taylor, Edd V.;DesPortes, Kayla
• 通讯作者: DesPortes, Kayla

A Computational Design Pipeline to Fabricate Sensing Network Physicalizations

• DOI: 10.1109/tvcg.2023.3327198
• 发表时间: 2024-01-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Bae，S. Sandra;Fujiwara，Takanori;Szafir，Danielle Albers
• 通讯作者: Szafir，Danielle Albers