We tested the hypothesis that underrepresented students in active-learning classrooms experience narrower achievement gaps than underrepresented students in traditional lecturing classrooms, averaged across all science, technology, engineering, and mathematics (STEM) fields and courses. We conducted a comprehensive search for both published and unpublished studies that compared the performance of underrepresented students to their overrepresented classmates in active-learning and traditional-lecturing treatments. This search resulted in data on student examination scores from 15 studies (9,238 total students) and data on student failure rates from 26 studies (44,606 total students). Bayesian regression analyses showed that on average, active learning reduced achievement gaps in examination scores by 33% and narrowed gaps in passing rates by 45%. The reported proportion of time that students spend on in-class activities was important, as only classes that implemented high-intensity active learning narrowed achievement gaps. Sensitivity analyses showed that the conclusions are robust to sampling bias and other issues. To explain the extensive variation in efficacy observed among studies, we propose the heads-and-hearts hypothesis, which holds that meaningful reductions in achievement gaps only occur when course designs combine deliberate practice with inclusive teaching. Our results support calls to replace traditional lecturing with evidence-based, active-learning course designs across the STEM disciplines and suggest that innovations in instructional strategies can increase equity in higher education.

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Synopsis Birds (Aves) exhibit exceptional and diverse locomotor behaviors, including the exquisite ability to balance on two feet. How birds so precisely control their movements may be partly explained by a set of intriguing modifications in their lower spine. These modifications are collectively known as the lumbosacral organ (LSO) and are found in the fused lumbosacral vertebrae called the synsacrum. They include a set of transverse canal-like recesses in the synsacrum that align with lateral lobes of the spinal cord, as well as a dorsal groove in the spinal cord that houses an egg-shaped glycogen body. Based on compelling but primarily observational data, the most recent functional hypotheses for the LSO consider it to be a secondary balance organ, in which the transverse canals are analogous to the semicircular canals of the inner ear. If correct, this hypothesis would reshape our understanding of avian locomotion, yet the LSO has been largely overlooked in the recent literature. Here, we review the current evidence for this hypothesis and then explore a possible relationship between the LSO and balance-intensive locomotor ecologies. Our comparative morphological dataset consists of micro-computed tomography (μ-CT) scans of synsacra from ecologically diverse species. We find that birds that perch tend to have more prominent transverse canals, suggesting that the LSO is useful for balance-intensive behaviors. We then identify the crucial outstanding questions about LSO structure and function. The LSO may be a key innovation that allows independent but coordinated motion of the head and the body, and a full understanding of its function and evolution will require multiple interdisciplinary research efforts.

Modern computational and imaging methods are revolutionizing the fields of comparative morphology, biomechanics, and ecomorphology. In particular, imaging tools such as X-ray micro computed tomography (µCT) and diffusible iodine-based contrast enhanced CT allow observing and measuring small and/or otherwise inaccessible anatomical structures, and creating highly accurate three-dimensional (3D) renditions that can be used in biomechanical modeling and tests of functional or evolutionary hypotheses. But, do the larger datasets generated through 3D digitization always confer greater power to uncover functional or evolutionary patterns, when compared with more traditional methodologies? And, if so, why? Here, we contrast the advantages and challenges of using data generated via (3D) CT methods versus more traditional (2D) approaches in the study of skull macroevolution and feeding functional morphology in bats. First, we test for the effect of dimensionality and landmark number on inferences of adaptive shifts during cranial evolution by contrasting results from 3D versus 2D geometric morphometric datasets of bat crania. We find sharp differences between results generated from the 3D versus some of the 2D datasets (xy, yz, ventral, and frontal), which appear to be primarily driven by the loss of critical dimensions of morphological variation rather than number of landmarks. Second, we examine differences in accuracy and precision among 2D and 3D predictive models of bite force by comparing three skull lever models that differ in the sources of skull and muscle anatomical data. We find that a 3D model that relies on skull µCT scans and muscle data partly derived from diceCT is slightly more accurate than models based on skull photographs or skull µCT and muscle data fully derived from dissections. However, the benefit of using the diceCT-informed model is modest given the effort it currently takes to virtually dissect muscles from CT scans. By contrasting traditional and modern tools, we illustrate when and why 3D datasets may be preferable over 2D data, and vice versa, and how different methodologies can complement each other in comparative analyses of morphological function and evolution.

Tests of phenotypic convergence can provide evidence of adaptive evolution, and the popularity of such studies has grown in recent years due to the development of novel, quantitative methods for identifying and/or measuring convergence. Two commonly used methods include (i) distance-based methods that measure morphological distances between lineages in phylomorphospace and (ii) fitting evolutionary models to morphological datasets to test whether lineages have evolved toward adaptive peaks. Here, we demonstrate that both types of convergence measures are influenced by the position of putatively convergent taxa in morphospace such that morphological outliers are statistically more likely to exhibit convergence by chance. A more substantial issue is that some methods will often misidentify divergent lineages as being convergent. These issues likely influence the results of many studies, especially those that focus on morphological outliers. To help address these problems, we developed a new distance-based method for measuring convergence that incorporates distances between lineages through time and minimizes the possibility of divergent taxa being misidentified as convergent. We advocate the use of this method when the phylogenetic tips of putatively convergent lineages are of the same or similar geologic ages (e.g., extant taxa), meaning that convergence among the lineages is expected to be synchronous. We conclude by emphasizing that all available convergence measures are imperfect, and researchers should recognize the limitations of these methods and use multiple lines of evidence when inferring and measuring convergence.