UCLA

This dissertation works builds rich understanding of why some community college developmental mathematics instructors perform better than their peers. We take up this work in Statway, a community college developmental mathematics course. To identify positive outer instructors, we use Hierarchical Linear Modeling and Empirical Bayes estimation to find instructors whose results are at least one standard deviation above the mean in math achievement with underprepared students. To build theoretical understanding of what accounts for positive deviance, we explore differences in instructor knowledge, with a particular focus on mathematical knowledge for teaching. Findings from our study of eleven Statway instructors (six positive outliers and five non-positive outliers) suggest that heightened empathy and heightened pedagogical agility in anticipating and responding to student challenges may be two key factors accounting for positive outliers’ successful outcomes. Our work offers several important contributions: it builds on our knowledge of mathematical knowledge for teaching by highlighting the role of instructor empathy and agility in deploying instructional knowledge; it highlights the importance of mathematical knowledge for teaching in the community college setting, a context not yet explored in the mathematical knowledge for teaching literature; it contributes rich understanding of effective instruction in community college developmental mathematics classrooms, especially in regards to instructional empathy and agility; and it offers new ways of studying variation in educational settings, as we examine the granular nature of positive outliers, focusing on the how and the why of what they do to understand the nature of their deviance.