EAGER: Smart and Connected Communities: Reducing the Friction in the L3 Connects Infrastructure through the Integration of SMART Technologies

EAGER：智能互联社区：通过集成智能技术减少 L3 连接基础设施中的摩擦

• 批准号: 1637358
• 负责人: Nichole Pinkard
• 金额: $ 21.33万
• 依托单位: DePaul University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637358&HistoricalAwards=false
• 关键词: EAGER; Smart; Connected; Communities; Reducing

This is an Early-concept Grant for Exploratory Research supporting research in Smart and Connected Communities. The research supported by the award is collaborative with research at the University of Colorado. The researchers are studying the use of technologies to enable communities to connect youth and youth organizations to effectively support diverse learning pathways for all students. These communities, the youth, the youth organizations, formal and informal education organizations, and civic organizations form a learning ecology. The DePaul University researchers will design and implement a smart community infrastructure in the City of Chicago to track real-time student participation in community STEM activities and to develop mobile applications for both students and adults. The smart community infrastructure will bring together information from a variety of sources that affect students' participation in community activities. These include geographic information (e.g., where the student lives, where the activities take place, the student transportation options, the school the student attends), student related information (e.g., the education and experience background of the student, the economic status of the student, students' schedules), and activity information (e.g., location of activity, requirements for participation). The University of Colorado researchers will take the lead on analyzing these data in terms of a community learning ecologies framework and will explore computational approaches (i.e., recommender systems, visualizations of learning opportunities) to improve youth exploration and uptake of interests and programs. These smart technologies are then used to reduce the friction in the learning connection infrastructure (called L3 for informal, formal, and virtual learning) to enable the student to access opportunities for participation in STEM activities that are most feasible and most appropriate for the student. Such a flexible computational approach is needed to support the necessary diversity of potential recommendations: new interests for youth to explore; specific programs based on interests, friends' activities, or geographic accessibility; or programs needed to "level-up" (develop deeper skills) and complete skills to enhance youths' learning portfolios. Although this information was always available, it was never integrated so it could be used to serve the community of both learners and the providers and to provide measurable student learning and participation outcomes.The learning ecologies theoretical framework and supporting computational methods are a contribution to the state of the art in studying afterschool learning opportunities. While the concept of learning ecologies is not new, to date, no one has offered such a systematic and theoretically-grounded portfolio of measures for characterizing the health and resilience of STEM learning ecologies at multiple scales. The theoretical frameworks and concepts draw together multiple research and application domains: computer science, sociology of education, complexity science, and urban planning. The L3 Connects infrastructure itself represents an unprecedented opportunities for conducting "living lab" experiments to improve stakeholder experience of linking providers to a single network and linking youth to more expanded and varied opportunities. The University of Colorado team will employ three methods: mapping, modeling, and linking youth to STEM learning opportunities in school and out of school settings in a large urban city (Chicago). The recommender system will be embedded into youth and parent facing mobile apps, enabling the team to characterize the degree to which content-based, collaborative filtering, or constraint based recommendations influence youth actions. The project will result in two measurable outcomes of importance to key L3 stakeholder groups: a 10% increase in the number of providers (programs that are part of the infrastructure) in target neighborhoods and a 20% increase in the number of youth participating in programs.

这是一项探索性研究的早期概念资助，支持智能和互联社区的研究。该奖项支持的研究是与科罗拉多大学的研究合作进行的。研究人员正在研究如何利用技术使社区将青年和青年组织联系起来，从而有效地支持所有学生的多样化学习途径。这些社区、青年、青年组织、正规和非正规教育组织、民间组织形成了一个学习生态。德保罗大学的研究人员将在芝加哥市设计和实施智能社区基础设施，以跟踪学生实时参与社区 STEM 活动，并为学生和成人开发移动应用程序。智能社区基础设施将汇集影响学生参与社区活动的各种来源的信息。其中包括地理信息（例如，学生居住的地方、活动地点、学生交通选择、学生就读的学校）、学生相关信息（例如，学生的教育和经历背景、学生的经济状况）学生、学生的日程安排）和活动信息（例如活动地点、参与要求）。 科罗拉多大学的研究人员将带头根据社区学习生态框架分析这些数据，并将探索计算方法（即推荐系统、学习机会可视化），以改善青少年对兴趣和项目的探索和吸收。然后，这些智能技术用于减少学习连接基础设施（称为 L3，表示非正式、正式和虚拟学习）中的摩擦，使学生能够获得参与最可行且最适合学生的 STEM 活动的机会。需要这种灵活的计算方法来支持潜在建议的必要多样性：年轻人探索的新兴趣；基于兴趣、朋友的活动或地理可达性的特定计划；或“升级”（发展更深层次的技能）和完成技能以增强青少年学习组合所需的计划。尽管这些信息总是可用的，但它从未被整合，因此它可以用于服务学习者和提供者的社区，并提供可衡量的学生学习和参与成果。学习生态学理论框架和支持计算方法是对研究课外学习机会的最新技术。虽然学习生态的概念并不新鲜，但迄今为止，还没有人提供如此系统且有理论依据的措施组合，用于在多个尺度上表征 STEM 学习生态的健康和复原力。理论框架和概念汇集了多个研究和应用领域：计算机科学、教育社会学、复杂性科学和城市规划。 L3 Connects 基础设施本身代表了进行“生活实验室”实验的前所未有的机会，以改善利益相关者将提供商连接到单一网络以及将年轻人连接到更广泛和多样化的机会的体验。科罗拉多大学团队将采用三种方法：绘制地图、建模以及将青少年与大城市（芝加哥）的学校内和校外环境中的 STEM 学习机会联系起来。推荐系统将嵌入到面向青少年和家长的移动应用程序中，使团队能够表征基于内容、协作过滤或基于约束的推荐对青少年行为的影响程度。该项目将带来两个对主要 L3 利益相关者群体具有重要意义的可衡量成果：目标社区的提供者（属于基础设施一部分的项目）数量增加 10%，参与项目的青年人数增加 20% 。