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 We conducted a comprehensive search for both published and unpublished studies that compared the performance of underrepresented students to their overrepresented classmates in active-learning and traditional-lecturing treatments.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.

Current molecular methods of species delimitation are limited by the types of species delimitation models and scenarios that can be tested. Bayes factors allow for more flexibility in testing non-nested species delimitation models and hypotheses of individual assignment to alternative lineages. Here, we examined the efficacy of Bayes factors in delimiting species through simulations and empirical data from the Sceloporus scalaris species group. Marginal-likelihood scores of competing species delimitation models, from which Bayes factor values were compared, were estimated with four different methods: harmonic mean estimation (HME), smoothed harmonic mean estimation (sHME), path-sampling/thermodynamic integration (PS), and stepping-stone (SS) analysis. We also performed model selection using a posterior simulation-based analog of the Akaike information criterion through Markov chain Monte Carlo analysis (AICM). Bayes factor species delimitation results from the empirical data were then compared with results from the reversible-jump MCMC (rjMCMC) coalescent-based species delimitation method Bayesian Phylogenetics and Phylogeography (BP&P). Simulation results show that HME and sHME perform poorly compared with PS and SS marginal-likelihood estimators when identifying the true species delimitation model. Furthermore, Bayes factor delimitation (BFD) of species showed improved performance when species limits are tested by reassigning individuals between species, as opposed to either lumping or splitting lineages. In the empirical data, BFD through PS and SS analyses, as well as the rjMCMC method, each provide support for the recognition of all scalaris group taxa as independent evolutionary lineages. Bayes factor species delimitation and BP&P also support the recognition of three previously undescribed lineages. In both simulated and empirical data sets, harmonic and smoothed harmonic mean marginal-likelihood estimators provided much higher marginal-likelihood estimates than PS and SS estimators. The AICM displayed poor repeatability in both simulated and empirical data sets, and produced inconsistent model rankings across replicate runs with the empirical data. Our results suggest that species delimitation through the use of Bayes factors with marginal-likelihood estimates via PS or SS analyses provide a useful and complementary alternative to existing species delimitation methods.

Sequence capture and restriction site associated DNA sequencing (RADseq) are popular methods for obtaining large numbers of loci for phylogenetic analysis. These methods are typically used to collect data at different evolutionary timescales; sequence capture is primarily used for obtaining conserved loci, whereas RADseq is designed for discovering single nucleotide polymorphisms (SNPs) suitable for population genetic or phylogeographic analyses. Phylogenetic questions that span both “recent” and “deep” timescales could benefit from either type of data, but studies that directly compare the two approaches are lacking. We compared phylogenies estimated from sequence capture and double digest RADseq (ddRADseq) data for North American phrynosomatid lizards, a species-rich and diverse group containing nine genera that began diversifying approximately 55 Ma. Sequence capture resulted in 584 loci that provided a consistent and strong phylogeny using concatenation and species tree inference. However, the phylogeny estimated from the ddRADseq data was sensitive to the bioinformatics steps used for determining homology, detecting paralogs, and filtering missing data. The topological conflicts among the SNP trees were not restricted to any particular timescale, but instead were associated with short internal branches. Species tree analysis of the largest SNP assembly, which also included the most missing data, supported a topology that matched the sequence capture tree. This preferred phylogeny provides strong support for the paraphyly of the earless lizard genera Holbrookia and Cophosaurus, suggesting that the earless morphology either evolved twice or evolved once and was subsequently lost in Callisaurus.