Measuring Collaboration in Complex Computerized Performance Assessments

在STEM教育的基础研究ECR计划提供资金在关键的研究领域是必不可少的，广泛的和持久的。EHR寻求有助于综合，建立和/或扩大以下重点领域研究基础的建议：STEM学习，STEM学习环境，STEM劳动力发展和扩大STEM参与。ECR计划的特点是强调积累强有力的证据，以（a）理解，（B）建立理论来解释，（c）提出干预措施（和创新），以解决STEM兴趣，教育，学习和参与方面的持续挑战。这个项目的动机背景是，几乎所有的21世纪世纪技能的定义都包括协作解决问题的某些方面。通常，与这种合作相关的技能被单独对待。在这项研究中，研究人员将调查这些合作解决问题的技能互动。研究团队将利用学生在支持电路设计和电子学学习的虚拟环境中工作时自动生成的过程数据。本研究的技术方面将使用数据挖掘技术和专家判断来验证PI团队的推断。此外，该项目将从事两个关键相关的组成部分：（1）一个可推广的模式，将衡量学生的内容和协作解决问题（CPS）的技能，在计算机化的教育环境的发展，以及（2）的建设和验证的诊断学生档案所产生的模式。

The ECR program of fundamental research in STEM education provides funding in critical research areas that are essential, broad and enduring. EHR seeks proposals that will help synthesize, build and/or expand research foundations in the following focal areas: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The ECR program is distinguished by its emphasis on the accumulation of robust evidence to inform efforts to (a) understand, (b) build theory to explain, and (c) suggest interventions (and innovations) to address persistent challenges in STEM interest, education, learning, and participation. The motivational context for this project is that nearly all definitions of 21st century skills include some aspects of collaborative problem solving. Often the skills associated with such collaborations are treated separately. In this study the researchers will investigate these collaborative problem solving skills interactively. The research team will draw on process data automatically generated as students work in the virtual environments that support the learning of circuit design and electronics. The technical aspects of this research will use data mining techniques and expert judgment to validate the inferences of the PI team. In addition the project will engage two critically related components: (1) the development of a generalizable schema that will measure student content and collaborative problem solving (CPS) skills in computerized educational environments, and (2) the construction and validation of diagnostic student profiles generated by the schema.