Characterizing STEM Departmental Teaching Culture: A Literature Review of the How, the Why, and the What

This NSF-funded project systematically reviews how departmental teaching culture has been characterized in STEM higher education from 1995 to 2024. In doing so, priority is placed on addressing three key facets: (1) how is STEM departmental teaching culture characterized, (2) why are researchers motivated to study STEM departmental teaching culture, and (3) what objects are of interest to researchers studying this topic. A growing corpus of literature discusses STEM departmental teaching culture to varying degrees; however, teaching culture is defined and characterized inconsistently across this literature, and at times is implicitly assumed to be understood. For instance, teaching culture could be defined as assumptions and values related to teaching or, alternatively, as conditions that affect teaching. As such, there is a need to bring together definitions, measurements, and models so as to generate a cohesive and universal understanding of what departmental teaching culture is. Additionally, the underlying motivations and foci of this literature base vary greatly, from papers discussing peoples' perceptions of departmental teaching culture to articles describing departmental teaching culture change initiatives in targeted STEM courses. This great variety motivates a consolidating effort to identify the core objects of interest in research examining STEM departmental teaching culture, as well as the underlying motivation of researchers studying this subject. Initial findings suggest that change initiatives encompass a large portion of the current literature discussing STEM departmental teaching culture; as such, insight from this literature review can be leveraged to further promote teaching culture transformation in STEM departments via the identification of targeted strategies. Further, initial data suggests the presence of several core models for characterizing culture, such as Reinholz and Apkarian's Four Frames model, which in turn rely on different objects as markers; systematic categorization of these models may allow for easier identification of appropriate models for future researchers.