Synthesis and Design Workshop: Weaving the Fabric of Adaptive STEM Learning Environments Across Domains and Settings

综合与设计研讨会：跨领域和设置编织自适应 STEM 学习环境的结构

• 批准号: 1825070
• 负责人: Roy Pea
• 金额: $ 10万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825070&HistoricalAwards=false
• 关键词: Synthesis; Design; Workshop; Weaving; Fabric

This workshop is funded through the "Dear Colleague Letter: Principles for the Design of Digital Science, Technology, Engineering, and Mathematics (STEM) Learning Environments (NSF 18-017)." The goal of this workshop is to articulate a transformative vision of future STEM learning for diverse learners across domains and settings. It forges a nexus among the emerging (a) sciences of learning, (b) assessment, and (c) big data to formulate frameworks and tools for designing STEM learning environments. Taking an equity-first approach for broadening participation through innovative designs, this project convenes interdisciplinary teams to produce a white paper proposing forward-looking digitally-augmented STEM environments that bridge formal and informal learning contexts and are responsive to the needs to every learner. The white paper will also articulate a future research agenda that could lead to new breakthroughs at the Human-Technology Frontier. The open-invitation design workshop, strategically located at Stanford University, and dissemination through a public website and community outreach activities at key conferences in which these scholarly communities convene will ensure broad awareness of and access to these models, tools, frameworks, design principles, and research priorities for educators, researchers, and analysts. The workshop is designed to construct needed new collaborations with the learning sciences, psychometrics, and computer science to design integrative STEM learning environments with robust in-process measures of adaptive learning that address key aspects of deeper learning. It convenes innovators advancing the state-of-the-art in equity-focused, technology-enhanced STEM learning, educational data mining and learning analytics, and computational psychometrics, to develop innovative ways to design and scale for a future of integrated STEM learning in an era of big data. An infrastructure of generative new algorithms and knowledge models, psychometric models, and learner pathway models will emerge from project activities at the intersection of these disciplinary perspectives to transform learning and assessment designs by incorporating signals from multimodal learning analytics and software for multi-faceted measurement of academic competencies. The project scope will be guided by three questions: (1) How can learning environments for integrated STEM learning scale successful efforts across diverse student populations and bridge formal and informal learning contexts? (2) What innovative research methods, statistical techniques and modeling formalisms are necessary to embed theoretical models in data-driven computational approaches in order to capture, characterize and support causal claims about individual and team-based learning, especially for complex, multi-source streaming data? (3) How can multi-domain threaded learning progressions be created for integrated learning and assessment of STEM subjects?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.

该研讨会是通过“亲爱的同事信：数字科学，技术，工程和数学（STEM）学习环境的设计原则（NSF 18-017）”资助的。“本次研讨会的目标是为跨领域和跨环境的不同学习者阐明未来STEM学习的变革愿景。它在新兴的（a）学习科学，（B）评估和（c）大数据之间建立了联系，以制定设计STEM学习环境的框架和工具。该项目采取公平第一的方法，通过创新设计扩大参与，召集跨学科团队制作一份白色文件，提出前瞻性的数字增强STEM环境，连接正式和非正式的学习环境，并响应每个学习者的需求。这份白色文件还将阐明未来的研究议程，这可能会导致人类技术前沿的新突破。开放式邀请设计研讨会战略性地设在斯坦福大学，并通过公共网站和这些学术界召开的重要会议上的社区外展活动进行传播，将确保教育工作者、研究人员和分析人员广泛了解和获得这些模型、工具、框架、设计原则和研究重点。该研讨会旨在与学习科学，心理测量学和计算机科学建立所需的新合作，以设计具有强大的自适应学习过程中措施的综合STEM学习环境，以解决深度学习的关键方面。它召集创新者推进以公平为中心的最先进的技术增强的STEM学习，教育数据挖掘和学习分析，以及计算心理测量学，以开发创新的方法来设计和扩展在大数据时代的综合STEM学习的未来。生成新的算法和知识模型，心理测量模型和学习者路径模型的基础设施将从这些学科观点的交叉点的项目活动中出现，通过整合来自多模态学习分析和软件的信号来改变学习和评估设计，用于多方面测量学术能力。该项目的范围将由三个问题指导：（1）综合STEM学习的学习环境如何在不同的学生群体中推广成功的努力，并在正式和非正式的学习环境中架起桥梁？(2)什么创新的研究方法，统计技术和建模形式主义是必要的嵌入理论模型在数据驱动的计算方法，以捕捉，表征和支持因果索赔个人和团队为基础的学习，特别是复杂的，多源流数据？(3)如何为STEM科目的综合学习和评估创建多领域线程学习进展？该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。