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，高等教育中的工程专业必须保证课程既严格又公平。随着对工程专业需求的增加，基础工程课程的班级规模也在增加。越来越多的证据表明，这些课程对学生的成功构成了重大障碍，与大班相关的惩罚对女性和代表性不足的群体的影响尤为严重。此外，这些教育环境使得实施已知对学生学习更好的循证教学实践具有挑战性。该项目将建立一个学习型组织生态系统，这是一项基层工作，涉及教师、部门和机构支持结构之间的合作，共同发现问题，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名工科本科生大型基础课程的16名教师将受到影响。除了直接受到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、多样化的数据源来改善教育环境，响应了国家对本科工程变得更加数据驱动的呼吁。早期的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）、现有教师级数据（例如，过去的考试）和新收集的数据（例如，关于学生如何在高风险考试前/后花费时间的调查）的稳健流。将对这些数据源进行三角测量和分析，以供教员和研究小组使用。还将举办夏季研讨会，让教师和管理人员参与设计过程：(1)制定个人教师行动计划；(2)共同制定机构变革行动计划。研究工作集中在学习分析和教师变化的交叉点，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。研究小组由教育研究人员、工程教员和来自工程学院、制度效率学院和学习科学学院的行政领导组成。因此，团队不仅可以从研究的角度有计划地领导这项工作，而且还可以将项目开发的策略和结果制度化。

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.