Cortical pulling on astral microtubules positions the mitotic spindle in response to PAR polarity cues and G protein signaling in many systems. In Caenorhabditis elegans single-cell embryos, posterior spindle displacement depends on Galpha and its regulators GPR-1/2 and LIN-5. GPR-1/2 and LIN-5 are necessary for cortical pulling forces and become enriched at the posterior cortex, which suggests that higher forces act on the posterior spindle pole compared with the anterior pole. However, the precise distribution of cortical forces and how they are regulated remains to be determined. Using spindle severing, single centrosome assays, and centrosome fragmentation, we show that both the anterior and posterior cortices generate more pulling force than the lateral-posterior region. Lateral inhibition depends on LET-99, which inhibits GPR-1/2 localization to produce a bipolar GPR-1/2 pattern. Thus, rather than two domains of cortical force, there are three. We propose that the attenuation of lateral forces prevents counterproductive pulling, resulting in a higher net force toward the posterior that contributes to spindle elongation and displacement.

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.