Collaborative Research: FW-HTF-RM: Intelligent Facilitation for Teams of the Future via Longitudinal Sensing in Context

合作研究：FW-HTF-RM：通过上下文中的纵向感知为未来团队提供智能协助

• 批准号: 1928612
• 负责人: Sidney D'Mello
• 金额: $ 33.81万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928612&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; RM

In the information workplace of the future, teamwork will become increasingly critical and teamwork itself will be redefined. Teams will need to develop better skills in handling complex problems as routine work will be increasingly delegated to artificial intelligence (AI) technologies such as personal digital assistants. Teams will need to rapidly adapt to fluid membership and changing work structures with the growing gig economy, and as new workers enter the workforce bringing new cultural practices. Individuals will need to be able to perform effectively in heterogeneous teams as the workforce becomes more diverse and as globalization increases. The future of teamwork will require integration of technological advances to facilitate team performance, yet we are largely relying on tools and techniques from the 20th century for team facilitation. This project will develop and validate an intelligent (AI-based) team facilitator for information work utilizing sensing and dynamic intervention to promote better team coordination, higher performance, and ultimately lower worker burnout. The intelligent team facilitator will serve as a blueprint for a broad set of domains beyond information work, including medical care teams, control room settings, crisis management, and manufacturing, where team skills will be needed for interacting with AI, robots, and new technologies. The facilitator can also be used for training underrepresented groups to succeed in the workforce, a national priority. The present project utilizes sensor technologies for tracking team behavior in information workplaces in addition to traditional methods of studying teams using observations and self- reports. Longitudinal precision tracking of teams in situ with a suite of sensors can provide objective measures, can scale, and will enable a deep understanding of how teams respond to changing contexts, how teams form and integrate new members, and how they develop rhythms of teamwork. This project examines team diversity broadly, considering demographics, attitudes, circadian rhythms and personal responsibilities. The first aim of this project is to develop models of critical team states and processes (e.g., team cohesion, team coordination, team mood/affect), based on unobtrusive, continual, longitudinal sensing of physiology, behavior, and communication in a real-world context along with measures of individual differences to understand factors that lead to team effectiveness. This project will use risk mitigation strategies to safeguard privacy and security of data. The second aim of this project is to use those insights to develop an intelligent (AI-based) team facilitator. Performance of teams who use the intelligent team facilitator will be experimentally compared against matched controls in a longitudinal in situ study. The results will contribute to a new understanding on how 21st century teams can manage complexity, how team heterogeneity can lead to team effectiveness, and will identify successful strategies for team adaptability.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.

在未来的信息工作场所，团队合作将变得越来越重要，团队合作本身也将被重新定义。团队将需要开发更好的技能来处理复杂的问题，因为日常工作将越来越多地委托给人工智能（AI）技术，如个人数字助理。 随着零工经济的增长，以及新员工进入劳动力市场带来新的文化习俗，团队需要快速适应流动的成员和不断变化的工作结构。 随着员工队伍变得更加多样化和全球化程度的提高，个人需要能够在异质团队中有效地执行任务。团队合作的未来将需要整合技术进步来促进团队绩效，但我们在很大程度上依赖于世纪的工具和技术来促进团队绩效。该项目将开发和验证一个智能（基于人工智能）的团队服务器，用于信息工作，利用传感和动态干预来促进更好的团队协调，更高的绩效，并最终降低工作人员的倦怠。智能团队促进者将作为信息工作之外的广泛领域的蓝图，包括医疗团队，控制室设置，危机管理和制造，其中需要团队技能与人工智能，机器人和新技术进行交互。调解人还可用于培训代表性不足的群体，使其在劳动力队伍中取得成功，这是国家的一项优先事项。本计画除了利用观察与自我报告的传统团队研究方法外，也利用感测器技术追踪资讯工作场所中的团队行为。使用一套传感器对团队进行纵向精确跟踪，可以提供客观的测量，可以扩展，并将能够深入了解团队如何应对不断变化的环境，团队如何形成和整合新成员，以及他们如何发展团队合作的节奏。这个项目广泛地考察了团队的多样性，考虑了人口统计学，态度，昼夜节律和个人责任。本项目的第一个目标是开发关键团队状态和过程的模型（例如，团队凝聚力，团队协调，团队情绪/情感），基于在现实世界背景下对生理学，行为和沟通的不引人注目的，持续的，纵向的感知，沿着对个体差异的测量，以了解导致团队有效性的因素。该项目将使用风险缓解战略来保护数据的隐私和安全。该项目的第二个目标是利用这些见解来开发一个智能（基于AI的）团队促进者。在一项纵向原位研究中，将使用智能团队促进者的团队绩效与匹配的对照组进行实验性比较。研究结果将有助于对21世纪团队如何管理复杂性、团队异质性如何导致团队效率的新理解，并将确定团队适应性的成功策略。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers

• DOI: 10.1109/taffc.2022.3188006
• 发表时间: 2022-10-01
• 期刊: IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
• 影响因子: 11.2
• 作者: Booth, Brandon M.;Vrzakova, Hana;D'Mello, Sidney K.
• 通讯作者: D'Mello, Sidney K.

Emotional regularity: associations with personality, psychological health, and occupational outcomes

情绪规律：与人格、心理健康和职业结果的关联

• DOI: 10.1080/02699931.2021.1968797
• 发表时间: 2021
• 期刊: Cognition and Emotion
• 影响因子: 2.6
• 作者: D’Mello, Sidney K.;Gruber, June
• 通讯作者: Gruber, June

Recurrence Quantification Analysis of Eye Gaze Dynamics During Team Collaboration

团队协作过程中眼睛注视动态的循环量化分析

• DOI: 10.1145/3576050.3576113
• 发表时间: 2023
• 期刊: ACM
• 作者: Moulder, Robert;Booth, Brandon;Abitino, Angelina;D'Mello, Sidney
• 通讯作者: D'Mello, Sidney

Designing an Interactive Visualization System for Monitoring Participant Compliance in a Large-Scale, Longitudinal Study

设计交互式可视化系统，用于监测大规模纵向研究中参与者的依从性

• DOI: 10.1145/3411763.3443436
• 发表时间: 2021
• 期刊: CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
• 作者: Talkad Sukumar, Poorna;Breideband, Thomas;Martinez, Gonzalo J.;Caruso, Megan;Rose, Sierra;Steputis, Cooper;D'Mello, Sidney;Mark, Gloria;Striegel, Aaron
• 通讯作者: Striegel, Aaron

Sleep Patterns and Sleep Alignment in Remote Teams during COVID-19

• DOI: 10.1145/3555217
• 发表时间: 2022-11
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Thomas Breideband;Gonzalo J. Martínez;P. Sukumar;Megan Caruso;Sidney K. D’Mello;A. Striegel;Gloria Mark