Predicting implementation of active learning by tenure-track teaching faculty using robust cluster analysis

• Published in: International journal of STEM education Vol. 9; no. 1; p. 49
• Main Authors: Denaro, Kameryn, Kranzfelder, Petra, Owens, Melinda T., Sato, Brian, Zuckerman, Austin L., Hardesty, Rebecca A., Signorini, Adriana, Aebersold, Andrea, Verma, Mayank, Lo, Stanley M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 28-07-2022; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Academic Rank (Professional); Active Learning; Algorithms; Class Size; Classroom Observation Techniques; Classrooms; Cluster analysis; College Faculty; Colleges & universities; COPUS; Education; Educational Technology; Gender Differences; Geographic Location; Higher education; Intellectual Disciplines; Learning Strategies; Lecturer with security of employment; Machine learning; Mathematics Education; Multivariate Analysis; Nontenured Faculty; Observation; Observational learning; Physical sciences; Professor of Teaching; Robustness (mathematics); Science Education; STEM; STEM Education; Teacher Characteristics; Teacher Researchers; Teachers; Teaching; Teaching Experience; Technical education; Technology education; Tenure; Undergraduate Study; University faculty; California; United States > US; Robust clustering; Higher education; Active learning; Lecturer with security of employment; COPUS; Teaching focused faculty; Professor of Teaching; Ensemble methods; Teaching Professor; STEM
• ISSN: 2196-7822; 2196-7822
• DOI: 10.1186/s40594-022-00365-9

Background
The 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.
Results
We 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.
Conclusions
The 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.