FW-HTF-RM: Intelligent Social Network Interventions to Augment Human Cognition for Interdisciplinary Interactions in Project Teams

建筑、工程和施工（AEC）行业的项目团队通常是临时的、高度复杂的多团队系统。他们需要顺利协调和整合的想法，而众多的个人在一个复杂的社会网络结构中互动，在子团队和项目团队的边界内外的学科和组织。有了这个动机，一个由工程、建筑管理、计算机科学、教育、社交网络、组织心理学和经济学专家组成的跨学科团队将开发一个智能社交网络干预的研究模型。通过增强人类认知和现实世界AEC和学生团队中多团队系统的功能，该模型将使个人能够发展未来在复杂社会系统中工作所需的技能，并通过改善学生成果，个人技能和项目成果提供短期和长期的经济和社会效益。该项目的成功完成将提供一个实用的系统，为个人和组织提供足够的手段，以促进多团队协调和项目效率。AEC项目团队通过其建筑环境产品对社会，经济和环境产生长期影响，因此，工人必须发展知识和技能，以支持在复杂的社会和任务结构中高度相互依赖的工作贡献。该项目的结果将对立即参加项目团队的AEC工人的生产力产生重大的积极影响，并将通过改善建筑环境扩展到广泛的劳动力。它将有助于组织，工程和研发团队的科学跨行业，现在和未来采用复杂的多团队系统。 将开发新的学习模块，用于基于项目的教学和学习，其中包括智能社会网络干预措施，并通过外联网站传播，以帮助培训未来的工作人员。这是利用技术提高人们对团队工作方式的敏感性，并不断提高他们的技能，以改善项目绩效、个人学习和未来工作的进步。虽然社会网络分析研究已经从各种角度进行，但几乎没有做什么来获得“可操作”的见解，并将这些见解作为干预措施来改善沟通，特别是从工作环境中。这构成了“动态（社会）网络重新布线”的基础，不仅基于人类行为，还基于工作环境，即，工作的目标，通过多个周期之间交替检查和干预网络的行为和背景。为了实现这些目标，研究团队将使用即时和机器/深度学习支持的社交网络干预措施来帮助个人发展未来工作所需的技能，并促进短期和长期的经济和社会效益。跨学科研究团队制定了一个纵向的比较研究设计，涉及现实世界的AEC团队以及课堂，学生团队测试平台，其中相同数量的案例将接受手动，机器学习支持，并且没有社交网络干预。 补充最近的网络干预研究，这个项目的重点是复杂和临时的多团队系统。在研究设计中的学生团队将有助于理解更小的，组织内的，多团队系统中的子团队动态和明天的真实工人和团队的出现。该设计将使用多模态图神经模型来自动识别糟糕的团队运作指标，以便通过增强人类对多团队协调的认知来诊断问题并促进干预措施。该设计可以积累从过去的学习中获得的知识，并将其应用于未来的学习，即使是在新的领域。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Project teams in the Architecture, Engineering, and Construction (AEC) industry are typically temporary and highly complex, multi-team systems. They require smooth coordination and integration of ideas while numerous individuals interact in a complex social network structure at sub-team and project team boundaries within and outside of their disciplines and organizations. With this motivation, a trans-disciplinary team of engineering, construction management, computer science, education, social networks, organizational psychology, and economics experts will develop a research model of intelligent social network interventions. By augmenting human cognition and the functioning of multi-team systems in real-world AEC and student teams, this model will enable individuals to develop the skills needed for future of work in complex social systems, and provide short and long-term economic and social benefits via improvements in student outcomes, individuals' skills, and project outcomes. The successful completion of the project will offer a practical system, equipping individuals and organizations with sufficient means to facilitate multi-team coordination and project effectiveness. AEC project teams have long-term social, economic, and environmental impacts through their built environment products and so, it is critical for workers to develop knowledge and skills that support highly interdependent work contributions in complex social and task structures. The results from this project will have a significant positive impact in the productivity of AEC workers that immediately take part in project teams, and will extend to a broad range of workforce via improvements in built environments. It will contribute to the science of organizations, engineering, and R&D teams across industries that employ complex multi-team systems now and in the future. New learning modules for project-based teaching and learning that incorporate intelligent social network interventions will be developed and disseminated through an outreach website to help train future workers. This is an advancement in the use of technology to sensitize humans on how teams work and continuously improve their skills for improved project performance, individual learning, and future of work.While social network analysis research has been carried out from various perspectives, little has been done to derive "actionable" insights and use these insights as intervention to improve communication, especially from the context of work. This forms the basis for "dynamic (social) network rewiring" based not only on human behavior but also the work context, i.e., the goals of the work, via multiple cycles alternating between examining and intervening the network for behavior and context. To achieve these goals, the researcher team will use immediate and machine/deep learning enabled social network interventions to help individuals develop the skills needed for future of work and facilitate short and long-term economic and social benefits. The trans-disciplinary research team has formulated a longitudinal, comparative research design involving real-world AEC teams as well as classroom, student-team test-beds, where equal numbers of cases are to receive manual, machine learning bolstered, and no social network interventions. Complementing the recent network intervention studies, this project focuses on complex and temporary multi-team systems. Student teams in the study design will contribute to the understanding of smaller, intra-organizational, sub-team dynamics in multi-team systems and emergence of tomorrow's authentic workers and teams. The design will use multi-modal graph neural models to automate recognition of poor team functioning metrics so that problems can be diagnosed and interventions can be facilitated via augmentation of human cognition for multi-team coordination. The design can accumulate knowledge obtained from past learning and adapt it for future learning, even in new domains.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.