Investing in Instructors: Creating Intelligent Feedback Loops in Large Foundational Courses for Undergraduate Engineering

多样化和高技能的工程劳动力在保持经济竞争力和保护国家安全方面发挥着关键作用。为了实现这些目标，高等教育中的工程课程必须保证课程既严格又公平。随着对工程专业需求的增加，基础工程课程的部分规模也在增加。越来越多的证据表明，这类课程是学生取得成功的重大障碍，与大班有关的处罚可能对妇女和代表性不足的群体产生不成比例的影响。此外，这些教育环境使得实施以证据为基础的教学实践具有挑战性，这些实践对学生的学习更好。该项目将建立一个学习型组织生态系统-涉及教师、部门和机构支持结构参与的基层努力，以协作确定问题，并持续、系统地提高工程课程的质量和公平性。在这个项目中，负责教授大约4800名本科工程专业学生大型基础课程的16名教师将受到影响。除了直接受影响的教师和学生之外，研究成果和项目成果将被广泛分享，以便其他教师和管理人员也可以同样地改善他们的教育事业。该项目通过探索如何利用现有的多样化数据源来改善教育环境，以响应国家对本科生工程变得更加数据驱动的呼吁。早期的努力将集中在创建智能反馈回路，现有机构数据的强大流（例如，历史成绩单数据，学生评价），现有的教师级数据（例如，过去的检查），以及新收集的数据（例如，关于学生如何在高风险考试前后花费时间的调查）。这些数据源将被三角测量和分析的方式，可以由教师和研究团队使用。暑期讲习班也将进行参与设计过程中的教师和管理人员：（1）建立个人教师行动计划和（2）集体构建一个机构变革行动计划。研究工作集中在学习分析和教师变化的交叉点上，以告知其他人如何有效地利用机构数据来改善STEM本科教育系统。研究团队由教育研究人员，工程学院和行政领导人从工程，机构效率和学习科学学院。因此，该团队不仅可以从研究的角度以编程方式领导这项工作，而且还可以将项目开发的战略和成果制度化。

A diverse and highly skilled engineering workforce plays a critical role in maintaining economic competitiveness and protecting national security. To achieve these aims, engineering programs in higher education must guarantee that curricula are both rigorous and equitable. As demand for engineering majors increases, so too do section sizes for foundational engineering courses. There is growing evidence that such courses represent significant barriers to student success and that the penalties associated with large classes can disproportionately affect women and underrepresented groups. Further, these educational environments make it challenging to implement evidence-based teaching practices known to be better for student learning. This project will build a learning organization ecosystem -- a grassroots effort involving engagement between faculty and departmental and institutional support structures to collaboratively identify problems and continuously, systematically improve the quality and equitability of the engineering curricula. During this project, sixteen instructors responsible for teaching approximately 4800 undergraduate engineering students in large foundational courses will be impacted. Beyond the instructors and the students directly impacted, research findings and project outcomes will be shared broadly so that other faculty and administrators might similarly improve their educational enterprise.This project responds to national calls for undergraduate engineering to become more data-driven by exploring how existing, diverse data sources can be leveraged to enhance educational environments. Early efforts will focus on creating intelligent feedback loops, robust streams of existing institutional data (e.g., historical transcript data, student evaluations), existing instructor-level data (e.g., past exams), and newly collected data (e.g., surveys about how students spend time pre/post high-stakes tests). Such data sources will be triangulated and analyzed in a way that can be used by the instructors and the research team. Summer workshops will also be conducted to engage faculty and administrators in a participatory design process: (1) to build individual instructor action plans and (2) to construct an institutional change action plan collectively. Research efforts center at the intersection of learning analytics and faculty change to inform how others might productively leverage institutional data to improve the STEM undergraduate education system. The research team consists of educational researchers, engineering faculty, and administrative leaders from the college of engineering, institutional effectiveness, and learning sciences. Thus, the team is well-poised to not only lead this effort programmatically and from a research perspective, but also institutionalize project-developed strategies and outcomes.