Instructor Talk—noncontent language used by instructors in classrooms—is a recently defined and promising variable for better understanding classroom dynamics. Having previously characterized the Instructor Talk framework within the context of a single course, we present here our results surrounding the applicability of the Instructor Talk framework to noncontent language used by instructors in novel course contexts. We analyzed Instructor Talk in eight additional biology courses in their entirety and in 61 biology courses using an emergent sampling strategy. We observed widespread use of Instructor Talk with variation in the amount and category type used. The vast majority of Instructor Talk could be characterized using the originally published Instructor Talk framework, suggesting the robustness of this framework. Additionally, a new form of Instructor Talk—Negatively Phrased Instructor Talk, language that may discourage students or distract from the learning process—was detected in these novel course contexts. Finally, the emergent sampling strategy described here may allow investigation of Instructor Talk in even larger numbers of courses across institutions and disciplines. Given its widespread use, potential influence on students in learning environments, and ability to be sampled, Instructor Talk may be a key variable to consider in future research on teaching and learning in higher education.

Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.active learning | evidence-based teaching | science education | lecture | assessment C urrent college STEM (science, technology, engineering, and mathematics) teaching in the United States continues to be lecture-based and is relatively ineffective in promoting learning (1, 2). Undergraduate instructors continue to struggle to engage, effectively teach, and retain postsecondary students, both generally and particularly among women and students of color (3, 4). Federal analyses suggest that a 10% increase in retention of undergraduate STEM students could address anticipated STEM workforce shortfalls (5). Replacing the standard lecture format with more active teaching strategies has been shown to increase

A collaborative professional development program that engaged nearly 90% of faculty in a biology department in more than 40 hours of training on scientific teaching was instituted. Participating instructors integrated active learning in their courses, as shown through a variety of methods, and reported positive effects on teaching and departmental community.

In this study, we expand upon the biogeography of biological soil crusts (BSCs) and provide molecular insights into the microbial community and biochemical dynamics along the vertical BSC column structure, and across a transect of increasing BSC surface coverage in the central Mojave Desert, CA, United States. Next generation sequencing reveals a bacterial community profile that is distinct among BSCs in the southwestern United States. Distribution of major phyla in the BSC topsoils included Cyanobacteria (33 ± 8%), Proteobacteria (26 ± 6%), and Chloroflexi (12 ± 4%), with Phormidium being the numerically dominant genus. Furthermore, BSC subsurfaces contained Proteobacteria (23 ± 5%), Actinobacteria (20 ± 5%), and Chloroflexi (18 ± 3%), with an unidentified genus from Chloroflexi (AKIW781, order) being numerically dominant. Across the transect, changes in distribution at the phylum (p < 0.0439) and genus (p < 0.006) levels, including multiple biochemical and geochemical trends (p < 0.05), positively correlated with increasing BSC surface coverage. This included increases in (a) Chloroflexi abundance, (b) abundance and diversity of Cyanobacteria, (b) OTU-level diversity in the topsoil, (c) OTU-level differentiation between the topsoil and subsurface, (d) intracellular ATP abundances and catalase activities, and (e) enrichments in clay, silt, and varying elements, including S, Mn, Co, As, and Pb, in the BSC topsoils. In sum, these studies suggest that BSCs from regions of differing surface coverage represent early successional stages, which exhibit increasing bacterial diversity, metabolic activities, and capacity to restructure the soil. Further, these trends suggest that BSC successional maturation and colonization across the transect are inhibited by metals/metalloids such as B, Ca, Ti, Mn, Co, Ni, Mo, and Pb.