Using data to Inform, Drive and Scale STEM Instructional Reform
At UC Davis, we've been trying to understand how the instructional system, from individual courses to whole programs, functions to support, and at times impair, successful STEM (Science, Technology, Engineering, and Mathematics) student outcomes for an ever increasingly diverse population. Examples include data-driven methods and tools, such as a novel visualization system for charting the pathways that students take through a university (the "Ribbon Tool") and a unique, quantitative classroom observation tool ("GORP").
In this discussion, I will present examples of our efforts, to:
- Understand and address incoming student preparation and course placement practices for both incoming freshmen and transfers
- Understand where and how student course path differences and performance disparities arise
- Study the impact of grading practices in large introductory courses on student outcomes
- Measure and impact student learning in introductory chemistry and biology courses at UCD
- Create tools to help understand and foster action to improve the instructional system