DIP: Developing Crosscutting Concepts in STEM with Simulation and Embodied Learning

网络学习和未来学习技术计划资助的努力将有助于展望下一代学习技术，并促进我们对人们在技术丰富的环境中如何学习的了解。开发和实施(DIP)项目建立在概念验证工作的基础上，这些工作展示了拟议的新型学习技术的可能性，项目团队建立和完善了拟议创新的最低可行范例，使他们能够了解未来应如何设计和使用这种技术，并使他们能够回答关于人们如何学习、如何促进或评估学习、和/或如何为学习而设计的问题。这项建议使用了多模式沉浸式界面的进步，例如允许运动检测的传感器(如微软Kinect)，以检查关于学习者在理解科学概念时如何用他们的身体思考的问题，比如“尺度”或“变化率”。该项目将帮助创建“体现学习的模拟剧场”，即拥有沉浸式技术的房间，允许学生与科学模拟互动，并通过移动身体同时表达想法。研究将考察学生使用的手势是否从一个科学学科传递到另一个科学学科，以及这种类型的互动是否有助于他们将他们在一个科学领域的知识转移到其他科学领域。在项目结束时，我们应该有一个技术平台，可以用来帮助研究学生如何使用手势来理解科学概念，2.关于这个工具在多大程度上支持跨学科学习的信息，以及3.关于人们如何用身体思考的新颖心理学研究。这个项目寻求扩展和提炼体现学习和体现设计的新兴理论。具体化互动显示出在特定STEM概念中增加学习的希望，但关于身体运动和手势如何促进理解可能促进学习迁移的抽象和交叉概念的了解较少。这个项目明确地研究了与计算机模拟的持久具体化交互方案是否使学习者更容易参与和学习新的STEM主题的新模拟。该项目还将在计算手势识别和处理方面取得智力进步，例如通过单实例机器学习算法、实时训练和辅助手势建模来捕获全身手势，以创建高度灵活的手势学习环境，从而能够基于单个主题进行培训，而不必建立大型手势数据库以实现可靠的识别。通过开发(1)用于识别学习手势的易于使用、低成本和高度可重新配置的系统，以及(2)一套集成的学习模拟，该系统依赖于具体化的交互来使用一致的接口方案来调查广泛的STEM主题，该项目将能够调查如何使用手势一致性来支持学习者对STEM学科的概念。研究将在最初阶段对12-15名中学生进行研究，以帮助确定跨学科手势隐喻的候选手势。随后的三次迭代将在每次迭代中使用大约50名学生，以检查与系统的互动是否可以利用具体化的隐喻，支持将学习从STEM模拟领域转移到其他领域，包括开发评估转移的工具。

The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that showed the possibilities of the proposed new type of learning technology, and project teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. This proposal uses advances in multimodal immersive interfaces, such as sensors that allow motion detection (like the Microsoft Kinect) to examine questions about how learners think with their bodies as they make sense of science concepts like 'scale' or 'rates of change'. The project will help create "simulation theatres for embodied learning," or rooms with immersive technology that allow students to interact with science simulations and simultaneously express ideas by moving their bodies. Research studies will examine whether gestures students use carry over from one science discipline to the next, and whether this type of interaction helps them transfer what they know in one science domain to others. At the end of the project, we should have 1. a technology platform that can be used to help research how students use gesture to understand science concepts, 2. information about how well this tool supports learning across disciplines, and 3. novel psychology research about how people think with their bodies.This project seeks to extend and refine emerging theories of embodied learning and embodied design. Embodied interactions have shown promise for increasing learning in specific STEM concepts, but there is less known about how body movement and gesture promote understanding abstract and crosscutting ideas that may facilitate learning transfer. This project examines explicitly whether persistent schemes of embodied interactions with computer simulations make it easier for learners to engage with, and learn from, new simulations of novel STEM topics. This project will also make intellectual advances in computational gesture recognition and processing, for instance through single-instance machine learning algorithms, real-time training, and modeling of paraterized gestures to capture full-body gestures to create a highly flexible gesture-learning environment that will enable training based on individual subjects without having to build a large database of gestures in order to achieve reliable recognition. By developing (1) an easy to use, low cost, and highly reconfigurable system for recognizing learning gestures and (2) an integrated set of learning simulations that rely on embodied interactions to investigate a broad range of STEM topics using consistent interface schemes, the project will be able to investigate how gestural congruency can be used to support learners' conceptions of STEM disciplines. Research studies will use 12-15 middle-school students in the initial phases to help identify candidate gestures for cross-disciplinary gestural metaphors. Three later iterations will use approximately 50 students per iteration to examine whether interacting with the system can engage embodied metaphors that support transfer of learning from the domain of a STEM simulation to other domains, including development of instruments for assessing transfer.