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.

Cognitive Task Analysis (CTA) interview technique is commonly used to elicit knowledge of subject-matter experts and to design instruction better focused on what experts don’t know they know. However, the knowledge of how to conduct an effective interview is, itself, largely implicit. In this study we performed protocol analysis on a set of interview transcripts from an expert CTA practitioner to elicit the cognitive processes of conducting CTA interviews. We also consulted expert CTA practitioners to identify the strategies that they used during the interviews. We present key strategies that were employed by the expert CTA practitioners to ensure comprehensiveness and accuracy in the information collected, such as looking for perceptual cues (e.g. considering verbs such as “determine”) to ascertain adequacy of SME’s responses and selection of follow-up questions. We present a production rule model as a detailed description of the cognitive processes underlying expert CTA interviewing

Biology education research (BER) is a growing field, as evidenced by the increasing number of publications in CBE-Life Sciences Education ( LSE) and expanding participation at the Society for the Advancement of Biology Education Research (SABER) annual meetings. To facilitate an introspective and reflective discussion on how research within LSE and at SABER has matured, we conducted a content analysis of LSE research articles ( n = 339, from 2002 to 2015) and SABER abstracts ( n = 652, from 2011 to 2015) to examine three related intraresearch parameters: research questions, study contexts, and methodologies. Qualitative data analysis took a combination of deductive and inductive approaches, followed by statistical analyses to determine the correlations among different parameters. We identified existing research questions, study contexts, and methodologies in LSE articles and SABER abstracts and then compared and contrasted these parameters between the two data sources. LSE articles were most commonly guided by descriptive research questions, whereas SABER abstracts were most commonly guided by causal research questions. Research published in LSE and presented at SABER both prioritize undergraduate classrooms as the study context and quantitative methodologies. In this paper, we examine these research trends longitudinally and discuss implications for the future of BER as a scholarly field.