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Structured classroom observation protocols provide instructors with data about their teaching practices, but instructors may not meaningfully engage with those data without guidance. To facilitate instructor reflection, educational developers from the Centers for Teaching and Learning (CTLs) and educational researchers from STEM departments across three campuses collaborated to design and implement a novel faculty professional development program that would promote reflection on teaching using instructors’ Classroom Observation Protocol for Undergraduate STEM (COPUS; Smith et al., 2013) data—a program we call data-informed professional development (DIPD). The program involved faculty completion of/participation in a teaching reflection, structured classroom observations from two course sessions, at least one meeting with CTL staff, an exit interview, and an opportunity to update their original teaching reflection. Through qualitatively coding the post-DIPD exit interviews, we found that instructors primarily reflected on their COPUS data with a desire to increase student engagement. Instructors also described being more open to making small changes to their courses, feeling supported to make changes to their teaching, and feeling that there was an important element of community-building in the DIPD program. And finally, instructors described how the DIPD experience was beneficial for promoting reflection on teaching practices, but the meeting portion was critical–providing data from the structured observations alone was not sufficient for a variety of reasons. Our study can serve as a teaching professional development model for how educational developers and education researchers can collaborate to prompt instructors to critically reflect on their teaching practices using structured observation protocols.  

Abstract BackgroundThe University of California system has a novel tenure-track education-focused faculty position called Lecturer with Security of Employment (working titles: Teaching Professor or Professor of Teaching). We focus on the potential difference in implementation of active-learning strategies by faculty type, including tenure-track education-focused faculty, tenure-track research-focused faculty, and non-tenure-track lecturers. In addition, we consider other instructor characteristics (faculty rank, years of teaching, and gender) and classroom characteristics (campus, discipline, and class size). We use a robust clustering algorithm to determine the number of clusters, identify instructors using active learning, and to understand the instructor and classroom characteristics in relation to the adoption of active-learning strategies. ResultsWe observed 125 science, technology, engineering, and mathematics (STEM) undergraduate courses at three University of California campuses using the Classroom Observation Protocol for Undergraduate STEM to examine active-learning strategies implemented in the classroom. Tenure-track education-focused faculty are more likely to teach with active-learning strategies compared to tenure-track research-focused faculty. Instructor and classroom characteristics that are also related to active learning include campus, discipline, and class size. The campus with initiatives and programs to support undergraduate STEM education is more likely to have instructors who adopt active-learning strategies. There is no difference in instructors in the Biological Sciences, Engineering, or Information and Computer Sciences disciplines who teach actively. However, instructors in the Physical Sciences are less likely to teach actively. Smaller class sizes also tend to have instructors who teach more actively. ConclusionsThe novel tenure-track education-focused faculty position within the University of California system represents a formal structure that results in higher adoption of active-learning strategies in undergraduate STEM education. Campus context and evolving expectations of the position (faculty rank) contribute to the symbols related to learning and teaching that correlate with differential implementation of active learning. 

The Classroom Observation Protocol for Undergraduate STEM (COPUS) provides descriptive feedback to instructors by capturing student and instructor behaviors occurring in the classroom. Due to the increasing prevalence of COPUS data collection, it is important to recognize how researchers determine whether groups of courses or instructors have unique classroom characteristics. One approach uses cluster analysis, highlighted by a recently developed tool, the COPUS Analyzer, that enables the characterization of COPUS data into one of seven clusters representing three groups of instructional styles (didactic, interactive, and student centered). Here, we examine a novel 250 course data set and present evidence that a predictive cluster analysis tool may not be appropriate for analyzing COPUS data. We perform a de novo cluster analysis and compare results with the COPUS Analyzer output and identify several contrasting outcomes regarding course characterizations. Additionally, we present two ensemble clustering algorithms: 1) k-means and 2) partitioning around medoids. Both ensemble algorithms categorize our classroom observation data into one of two clusters: traditional lecture or active learning. Finally, we discuss implications of these findings for education research studies that leverage COPUS data.