CHS: Small: Novel Technology Augmented Methods to Improve Team-based Engineering Education for Diverse Teams

该项目将开发一个自动化系统来度量核心团队流程。团队在今天的劳动力中越来越普遍，在团队中有效工作的能力是一种竞争优势。因此，从第一年的工程设计课程到高年级的顶点项目，以团队为基础的学习现在在大学的各个层次都很普遍。基于团队的学习的兴起在有效地提供这种学习模式的最佳效益方面产生了两个相互关联的挑战。首先，虽然教育工作者在评估技术学习方面已经成熟，比如结果和演示的质量，但他们在理解、衡量和教育学生有效的团队合作过程本身方面仍处于早期阶段。其次，基于团队的研究几乎完全依赖于自我报告测量和人类观察，这是容易出错和有问题的。因此，显然有必要扩充当前的方法，以便科学家和教育工作者能够更好地度量导致有效团队绩效的过程。这个项目不仅会影响以团队为基础的教育，还会更广泛地影响行为科学。通过发展客观测量重要心理构念的行为相关性的方法，行为科学将超越传统的研究工具。本研究以心理学理论和研究为基础，在工程技术的推动下，将利用两种形式的团队互动来理解与个人效率、团队动态以及多样性对个人和团队指标的影响相关的关键指标：(1)通过团队会议的视听记录测量的面对面互动，以及(2)通过团队共享的书面在线协作文档测量的工作互动。通过本研究开发的系统将包括从视听会议数据和在线文件中提取动作序列的新方法。数据将在三种团队环境下的一系列项目中收集：(1)有针对性的研究，团队组成90分钟的项目冲刺；(2)中期团队进行为期7周的工程设计实习，(3)长期团队进行为期一学期的工程设计课程。经过验证的心理评估和新指标将被收集并组装成一个广泛而多样的数据集。一个多层网络模型将被开发来表示在个体动作序列中收集的人类交互的不同模式。目的是提取与团队动态和角色出现的有意义的心理指标相关的网络特征。该奖项反映了美国国家科学基金会的法定使命，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。

This project will develop an automated system to measure core team processes. Teams are increasingly prevalent in today's workforce, and the ability to work effectively in teams is a competitive advantage. As a result, team-based learning is now widespread in universities across all levels, ranging from first-year engineering design courses to senior capstone projects. The rise of team-based learning has created two inter-related challenges in effectively delivering the optimal benefits of this learning modality. First, while educators have matured in evaluating technical learning, such as quality of results and presentation, they are still in the early stages of understanding, measuring, and educating students on the process of effective teamwork itself. Second, team-based research relies almost exclusively on self-report measures and human observation, which are error prone and problematic. Thus, there is a clear need to augment current methods so scientists and educators can better measure the processes that lead to effective team performance. The project will impact not only team-based education, but behavioral sciences more broadly. By developing methods to objectively measure behavioral correlates of important psychological constructs, behavioral sciences will go beyond the traditional tools for research.Grounded in psychological theory and research, and enabled by advances in engineering, this research will leverage two forms of team interactions to understand key metrics related to individual effectiveness, team dynamics, and the impact of diversity on both individual and team metrics: (1) in-person interactions measured from the audio-visual recordings of team meetings, and (2) work interactions measured from team's shared written online collaborative documents. The system developed through this research will include novel methods to extract action sequences from audio-visual meeting data and from the online documents. Data will be collected in a series of projects across three team environments: (1) targeted studies where teams are formed for 90-minute project sprints; (2) medium-term teams working on a 7-week engineering design internship, and (3) longer-term teams in a semester-long engineering design course. Validated psychological assessments and new metrics will be collected and assembled into an extensive and diverse dataset. A multilayer network model will be developed to represent the different modes of human interaction collected in the individual action sequences. The objective is to extract network features that correlate with meaningful psychological indicators of team dynamics and role emergence within teams.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.