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

这个项目将开发一个自动化系统来衡量核心团队的流程。 团队在当今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，以团队为基础的学习现在在各个层次的大学中都很普遍，从一年级的工程设计课程到高级顶点项目。以团队为基础的学习的兴起在有效提供这种学习方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，例如结果和演示的质量，但他们仍然处于理解，测量和教育学生有效团队合作过程本身的早期阶段。第二，团队研究几乎完全依赖于自我报告和人类观察，这是容易出错和有问题的。因此，有一个明确的需要，以扩大目前的方法，使科学家和教育工作者可以更好地衡量的过程，导致有效的团队绩效。该项目不仅将影响以团队为基础的教育，但行为科学更广泛。通过开发客观测量重要心理结构的行为相关性的方法，行为科学将超越传统的研究工具。基于心理学理论和研究，并通过工程学的进步，这项研究将利用两种形式的团队互动来理解与个人有效性，团队动力学以及多样性对个人和团队指标的影响相关的关键指标：（1）从团队会议的视听记录中测量的面对面互动，以及（2）从团队共享的书面在线协作文档中测量的工作互动。通过这项研究开发的系统将包括新的方法来提取动作序列的视听会议数据和在线文档。 数据将在三个团队环境中的一系列项目中收集：（1）有针对性的研究，其中团队形成90分钟的项目冲刺;（2）中期团队工作7周的工程设计实习，以及（3）长期团队在一个学期的工程设计课程。经过验证的心理评估和新的指标将被收集并组装成一个广泛而多样化的数据集。将开发一个多层网络模型来表示在个体动作序列中收集的人类交互的不同模式。该奖项反映了NSF的法定使命，并被认为是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进行评估的支持。

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.