Leveraging local data for reflective teaching in large classes

Abstract

In this paper, we discuss efforts to collect and analyse data at the local level to prompt reflective teaching in large foundational engineering courses. The work is in direct response to the growing demands and constraints of teaching in large classes that compromise the educational environment. We feel that motivated improvement-seeking institutions and academics must strive to innovate within the constraints. To do this, we argue that locally rooted data analytics offers unique opportunities to infuse data back into decision-making processes. As a demonstration of our claims, we discuss summaries of our team’s efforts within large foundational engineering mechanics courses at a public research institution in the United States. Specifically, we report on work to examine (1) Student Engagement and Performance (analysis of student surveys and course grades); (2) Performance Patterns Not Performance Points (analysis of course-level data); and (3) Understanding Courses in Context (analysis of existing institutional data).

Keywords:

• Data analytics
• learning analytics
• large classes
• reflective teaching
• engineering education

Acknowledgements

The authors would like to gratefully acknowledge the support of a 4-VA Collaborative Research Grant which supported some of the research summarised in this article.