Data-Driven, Human-in-the-Loop Support for Facilitating Participatory Learning Activities

数据驱动的人机交互支持，促进参与式学习活动

• 批准号: 1822864
• 负责人: Katherine McMillan
• 金额: $ 40.07万
• 依托单位: New York Hall of Science
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822864&HistoricalAwards=false
• 关键词: Data; Driven; Human; Loop; Support

Museums are increasingly developing digital learning experiences where visitors generate interesting usage data, but these data are rarely used to support learning. This project poses a "human-in-the-loop" model of facilitation where museum experts are provided with data visualizations and other feedback derived by mining exhibit use data. These visuals will be used as coaching support for facilitators as they help visitors make sense of the rich learning experiences that digital exhibits now offer. While this approach is immediately relevant to museum learning, human-in-the-loop systems represent a new take on cyberlearning systems that will be applicable to many other formal and informal STEM learning environments that demand immediate, just-in-time reflection that is guided by an expert observer.The intervention will be used in the context of an open-ended, participatory exhibit that invites up to 50 visitors at a time to manipulate a simulated ecosystem, with the goal of having visitors engage with concepts related to sustainability and complex systems by exploring emergent phenomena. Open-ended learning experiences are common to museum exhibits but are under-studied in the Educational Data Mining (EDM) community. This research will add to the growing EDM literature on open-ended problems and provide insights for how museum exhibits can be designed to supply data that can be useful for analytics and data mining. A goal of this research is to develop and refine a typology of the forms of information that museum and other facilitators working with real-time data can make sense of and act on, which could be used to inform both the analytics and the software design for future tools to facilitate learning.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.

博物馆越来越多地开发数字学习体验，游客可以生成有趣的使用数据，但这些数据很少用于支持学习。这个项目提出了一个“人在回路”的促进模型，博物馆专家提供数据可视化和其他反馈来自挖掘展览使用数据。这些视觉效果将被用作辅导员的辅导支持，因为它们帮助参观者理解数字展览现在提供的丰富学习体验。虽然这种方法与博物馆学习直接相关，但人在回路系统代表了对网络学习系统的一种新看法，将适用于许多其他正式和非正式的STEM学习环境，这些环境要求在专家观察员的指导下立即及时反思。参与性展览，每次邀请多达50名参观者操纵模拟生态系统，目的是让参观者通过探索新兴现象参与与可持续性和复杂系统相关的概念。开放式学习体验在博物馆展览中很常见，但在教育数据挖掘（EDM）社区中研究不足。这项研究将增加不断增长的开放式问题的EDM文献，并为如何设计博物馆展品提供对分析和数据挖掘有用的数据提供见解。这项研究的一个目标是开发和完善一种信息形式的类型学，博物馆和其他使用实时数据的促进者可以理解并采取行动，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Formative Fugues: Reconceptualizing Formative Feedback for Complex Systems Learning Environments

形成性赋格：重新概念化复杂系统学习环境的形成性反馈

• 发表时间: 2021
• 期刊: International journal of complexity in education
• 作者: Mallavarapu, A.;Lyons, L.;Uzzo, S.
• 通讯作者: Uzzo, S.

Exploration Maps, Beyond Top Scores: Designing Formative Feedback for Open-Ended Problems

探索地图，超越最高分：为开放式问题设计形成性反馈

• 发表时间: 2020
• 期刊: Proceedings of The 13th International Conference on Educational Data Mining
• 作者: Mallavarapu, A.

Design Considerations for Data-Driven Dashboards: Supporting Facilitation Tasks for Open-Ended Learning

数据驱动仪表板的设计注意事项：支持开放式学习的促进任务

• DOI: 10.1145/3334480.3382871
• 发表时间: 2020
• 期刊: CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Beheshti, Elham;Lyons, Leilah;Mallavarapu, Aditi;Wallingford, Betty;Uzzo, Stephen
• 通讯作者: Uzzo, Stephen

Connect-to-Connected Worlds: Piloting a Mobile, Data-Driven Reflection Tool for an Open-Ended Simulation at a Museum

连接到连接的世界：在博物馆试用移动、数据驱动的反射工具进行开放式模拟

• DOI: 10.1145/3290605.3300237
• 发表时间: 2019
• 期刊: CHI '19 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
• 作者: Mallavarapu, Aditi;Lyons, Leilah;Uzzo, Stephen;Thompson, Wren;Levy-Cohen, Rinat;Slattery, Brian
• 通讯作者: Slattery, Brian

Exploring the Utility of Social-Network-Derived Collaborative Opportunity Temperature Readings for Informing Design and Research of Large-Group Immersive Learning Environments

• DOI: 10.18608/jla.2022.7419
• 发表时间: 2022-03
• 期刊: J. Learn. Anal.
• 作者: Aditi Mallavarapu;L. Lyons;S. Uzzo