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

一支多元化和高技能的工程队伍在保持经济竞争力和保护国家安全方面发挥着至关重要的作用。为了实现这些目标，高等教育中的工程学课程必须保证课程既严格又公平。随着对工程专业的需求增加，基础工程课程的课程规模也在增加。越来越多的证据表明，这种课程是学生取得成功的重大障碍，与大班相关的惩罚可能会对女性和代表性不足的群体造成不成比例的影响。此外，这些教育环境使实施已知对学生学习更有利的循证教学实践具有挑战性。这个项目将建立一个学习型组织生态系统--这是一种基层努力，涉及教员与部门和机构支持结构之间的参与，以协作地发现问题，并持续、系统地提高工程学课程的质量和公平性。在这个项目中，16名负责在大型基础课程中教授大约4800名本科工程专业学生的讲师将受到影响。除了教师和直接受影响的学生之外，研究成果和项目成果将被广泛分享，以便其他教职员工和管理人员也可以类似地改进他们的教育事业。该项目响应国家呼吁本科生工程学变得更加数据驱动的呼吁，探索如何利用现有的、多样化的数据源来改善教育环境。早期工作将侧重于创建智能反馈循环、现有机构数据(例如，历史成绩单数据、学生评估)、现有教师级别数据(例如，过去的考试)和新收集的数据(例如，关于学生在高风险考试前/后如何花费时间的调查)的稳健流。这样的数据源将被三角化，并以一种教师和研究团队可以使用的方式进行分析。还将举办暑期讲习班，让教员和行政人员参与设计过程：(1)制定个别教师行动计划；(2)集体制定机构改革行动计划。研究工作集中在学习分析和教师变动的交叉点上，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。研究团队由工程、制度有效性和学习科学学院的教育研究人员、工程学教师和行政领导组成。因此，该团队不仅准备好从方案和研究角度领导这项工作，而且还将项目制定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.