Search for: All records

Research-intensive universities aim to conduct cutting-edge research while providing the knowledge and skills necessary to prepare students to excel in their respective fields. As student enrollments surge, many institutions have turned to hiring teaching-focused faculty. In the University of California (UC) system, there exists a unique position known as the Professor of Teaching (PoT). This position is tenure-eligible, and members are required to engage in classroom teaching, scholarly activities, and service responsibilities. To shed light on the background characteristics, roles and perceptions of the impact of teaching-focused faculty in research-intensive institutions, we collected survey data from STEM PoT faculty across the UC system. We employed a mixed methods approach, using descriptive and inferential statistics to analyze quantitative responses and thematic analysis to examine open-ended qualitative data. Our analysis shows that pre-tenure PoTs place greater emphasis on scholarly activities relative to their peers who have been in the role for longer. However, their training and the institutional resources provided may not align with expectations for scholarly activities. Additionally, we find that PoTs who engage in research perceive that they have a more significant impact on their colleagues’ teaching. This finding underscores the value of research, even for teaching-focused faculty. This study informs the evolving landscape of teaching-focused faculty within research-intensive universities and provides recommendations for administrators considering how to ensure that their institutions are fulfilling their educational mission. 

Despite the increasing diversity of undergraduate students in the United States, university faculty demographics, particularly in science, technology, engineering, and mathematics (STEM) fields, remain largely homogeneous, which is problematic for fostering an inclusive academic environment. We examined the hiring process for tenure-track teaching-focused faculty (TFF) positions, specifically within the University of California system, to develop and implement inclusive hiring practices that may promote greater faculty diversity. Through a series of faculty learning communities (FLCs), we developed and implemented inclusive hiring rubrics designed to better evaluate teaching excellence and ensure the recruitment of diverse faculty members. Our findings highlight the critical need for faculty diversity, particularly TFF who instruct in gateway introductory STEM courses, to enhance student outcomes by fostering more inclusive teaching practices and reducing racial disparities in academic achievement. We recommend that institutions adopt inclusive hiring practices, including the use of tailored hiring rubrics, to create a more equitable and supportive learning environment for all students. 

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.  

Embedding change agent individuals within STEM departments may drive instructional and pedagogical change efforts. This study seeks to assess whether tenure-track, teaching-focused faculty housed in STEM departments are perceived as influential on the instructional and pedagogical domains of their colleagues. 

Inquiry-based course experiences provide a scalable and equitable way to engage students in research. In this study, we describe how we introduced inquiry-based experiences to ten lower-division and upper-division courses across the biology curriculum at a regionally comprehensive public university serving the diverse population in a major metropolitan area. Student survey data suggest this redesign effectively developed students’ scientific skills and nurtured their sense of belonging. This project illustrates how inquiry-based experiences can be implemented sustainably across institutional context. 

Many science, technology, engineering, and math (STEM) community college students do not complete their degree, and these students are more likely to be women or in historically excluded racial or ethnic groups. In introductory courses, low grades can trigger this exodus. Implementation of high-impact study strategies could lead to increased academic performance and retention. The examination of study strategies rarely occurs at the community college level, even though community colleges educate approximately half of all STEM students in the United States who earn a bachelor’s degree. To fill this research gap, we studied students in two biology courses at a Hispanic-serving community college. Students were asked their most commonly used study strategies at the start and end of the semester. They were given a presentation on study skills toward the beginning of the semester and asked to self-assess their study strategies for each exam. We observed a significantly higher course grade for students who reported spacing their studying and creating drawings when controlling for demographic factors, and usage of these strategies increased by the end of the semester. We conclude that high-impact study strategies can be taught to students in community college biology courses and result in higher course performance. 

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.