Local Environmental Modeling: A Toolkit for Incorporating Place-Based Learning into Virtual Internships - A Scalable, Informal STEM Learning Environment

本地环境建模：将本地学习融入虚拟实习的工具包 - 可扩展的非正式 STEM 学习环境

• 批准号: 1713110
• 负责人: David Shaffer
• 金额: $ 199.97万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713110&HistoricalAwards=false
• 关键词: Local; Environmental; Modeling; Toolkit; Incorporating

In this project, education researchers, environmental scientists, and educators will develop a computer tool to let STEM educators and curriculum developers build local environmental science models. The system will use data about land use to automatically construct map-based simulations of any area in the United States. Users will be able to choose from a range of environmental and economic issues to include in these models. The system will create simulations that ask students to change to patterns of land use -- for example, increasing land zoned for housing, or open land, or industrial development -- to try to meet environmental and social goals. As a result, students will be able to learn about the interaction of environmental and economic issues relevant to their own city, town, neighborhood, or region. These map-based simulations will be incorporated into an existing science, technology, engineering, and mathematics (STEM) education tool, Land Science, in which learners work in a fictional planning office to study how zoning affects economic and environmental issues in a community. Research has shown that Land Science is mode effective when learners are exploring issues in an area near their home, and the current study will investigate how and why local simulations improve environmental science learning. This project is funded by the Advancing Informal STEM Learning (AISL) program which supports work to enhance learning in informal environments by funding innovative research, approaches, and resources for use in a variety of settings. In this project, the research team will build, test, and deploy a toolkit that will allow informal STEM educators and developers of informal STEM programming to easily adapt an existing environmental science learning environment, which consists of a place-based virtual internship in urban planning and ecology, to their local contexts, learning objectives, and learner populations. Land Science is a virtual internship in which young people explore the environmental and socio-economic impacts of land-use decisions. To do so, they play the role of interns at an urban planning firm developing a new land-use proposal for the city of Lowell, Massachusetts: they read reports, virtually visit sites, determine stakeholder priorities, and use a geographic information system (GIS) model to evaluate the socio-economic and environmental impacts of land-use choices. No one plan can satisfy all stakeholders, so learners must compromise to create an effective plan and justify their decisions. Land Science has been shown to improve civic engagement, interest in eco-social issues, and understanding of scientific models, but it is most effective when the location of the virtual internship is in or near the learners' home town. To improve the accessibility and impact of this effective learning intervention, the interdisciplinary research team, which includes learning scientists, land-use experts, and informal STEM educators, will develop a Local Environmental Modeling toolkit, which will allow educators to change the location of the simulation and the stakeholder groups, zoning codes, and environmental and socio-economic indicators included in the land-use model. The system will ensure that the model produced is functional, realistic, and appropriately complex. The localized versions of Land Science produced by informal STEM educators will be used in a range of contexts and locations, allowing the research team to study the effects of an online, place-based learning intervention on environmental science learning, STEM interest and motivation, and civic engagement.

在这个项目中，教育研究人员、环境科学家和教育工作者将开发一种计算机工具，让STEM教育工作者和课程开发人员建立当地的环境科学模型。该系统将使用有关土地使用的数据，自动构建美国任何地区的基于地图的模拟。用户将能够从一系列环境和经济问题中进行选择，以包括在这些模型中。该系统将创建模拟，要求学生改变土地使用模式--例如，增加住房、空地或工业发展用地--以努力实现环境和社会目标。因此，学生将能够了解与他们所在的城市、城镇、社区或地区相关的环境和经济问题的相互作用。这些基于地图的模拟将被纳入现有的科学、技术、工程和数学(STEM)教育工具-土地科学，在该工具中，学习者在虚构的规划办公室工作，研究分区如何影响社区的经济和环境问题。研究表明，当学习者在家附近的区域探索问题时，土地科学是有效的模式，目前的研究将调查当地模拟如何以及为什么改善环境科学学习。该项目由推进非正式STEM学习计划(AISL)资助，该计划通过资助各种环境中使用的创新研究、方法和资源来支持在非正式环境中加强学习的工作。在这个项目中，研究小组将建立、测试和部署一个工具包，使非正式STEM教育工作者和非正式STEM编程开发人员能够轻松地调整现有的环境科学学习环境，包括基于地点的城市规划和生态虚拟实习，以适应他们的当地背景、学习目标和学习者群体。土地科学是一种虚拟实习，让年轻人探索土地使用决策对环境和社会经济的影响。为此，他们扮演着一家城市规划公司的实习生的角色，为马萨诸塞州洛厄尔市制定一项新的土地使用方案：他们阅读报告，虚拟访问地点，确定利益相关者的优先事项，并使用地理信息系统(GIS)模型来评估土地使用选择的社会经济和环境影响。没有一个计划能让所有利益相关者满意，所以学习者必须妥协以创建一个有效的计划并证明他们的决定是合理的。土地科学已被证明可以提高公民的参与度、对生态社会问题的兴趣以及对科学模型的理解，但当虚拟实习地点在学习者的家乡或附近时，这种方法最有效。为了提高这一有效学习干预措施的可及性和影响，包括学习科学家、土地利用专家和非正式STEM教育工作者在内的跨学科研究小组将开发一个当地环境建模工具包，使教育工作者能够改变模拟的地点和土地利用模型中包括的利益相关者群体、分区代码以及环境和社会经济指标。该系统将确保产生的模型是实用的、现实的和适当的复杂的。由非正式STEM教育工作者编写的土地科学本地化版本将在一系列背景和地点中使用，使研究小组能够研究在线、基于地点的学习干预对环境科学学习、STEM兴趣和动机以及公民参与的影响。

项目成果

Zone It! A mobile application for practice- and place-based learning

区域它！

• 发表时间: 2019
• 期刊: Learning Sciences Graduate Student Conference
• 作者: Siebert-Evenstone, A;Swiecki, Z;Eagan, B;Sung, H;Brohinsky, J;Fogel, A;Wang, Y;Shaffer, D.W
• 通讯作者: Shaffer, D.W

Localizing Socio-Environmental Problem Solving

本地化社会环境问题解决

• 发表时间: 2022
• 期刊: Computersupported collaborative learning
• 作者: Ruis, A. R.;Siebert Evenstone, A. L.;Brohinsky, J.;Tan, Y.;Hinojosa, C.;Cai, Z.;Marquart, C.;Lark, T.;Barford, C.;Shaffer, D. W.
• 通讯作者: Shaffer, D. W.

Understanding when students are active-in-thinking through modeling-in-context

通过情境建模了解学生何时积极思考

• 发表时间: 2019
• 期刊: British journal of educational technology
• 影响因子: 6.6
• 作者: Swiecki, Z;Ruis, A. R;Gautam, D;Rus, V;Shaffer, D.W.
• 通讯作者: Shaffer, D.W.

Location, location, location: The effects of place in place-based simulations

位置，位置，位置：基于地点的模拟中地点的影响

• 发表时间: 2019
• 期刊: and Embedded Learning in Collaborative Settings:13th International Conference on Computer Supported Collaborative Learning
• 作者: Siebert-Evenstone, A. L;Shaffer, D. W.
• 通讯作者: Shaffer, D. W.

Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer

• DOI: 10.3390/rs13050968
• 发表时间: 2021-03-01
• 期刊: REMOTE SENSING
• 影响因子: 5
• 作者: Lark, Tyler J.;Schelly, Ian H.;Gibbs, Holly K.
• 通讯作者: Gibbs, Holly K.