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Post-course instructor ratings are a common practice at universities in Europe, Australia, and North America. Rather than solely describing teaching practice, however, such ratings may be associated with a range of non-pedagogical factors. We explored engineering students' instructor ratings at a large United States institution, investigating relationships between overall instructor rating and some of those non-pedagogical factors, including subject area, class size, and students' course grades. Instructor ratings were more favourable in humanities courses than in science or math courses, and students gave higher ratings to instructors who taught smaller class sizes. The strongest relationship existed between overall rating and students' course grades: students who received A's rated instructors an average 0.84 points higher on a 6-point scale than students who received F's. Students who withdrew from a course provided the lowest instructor ratings. When instructor ratings are used as metrics of teaching ability in discussions related to promotion, tenure, or salary adjustments, there is an inherent inequity in the system that must be acknowledged beyond already well-documented biases, as non-pedagogical factors often outside of the instructor's control may be significantly associated with those ratings. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]