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 We conducted a comprehensive search for both published and unpublished studies that compared the performance of underrepresented students to their overrepresented classmates in active-learning and traditional-lecturing treatments.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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Biotic interactions present a challenge in determining whether species distributions will track climate change. Interactions with competitors, consumers, mutualists, and facilitators can strongly influence local species distributions, but few studies assess how and whether these interactions will impede or accelerate climate change–induced range shifts. In this paper, we explore how ecologists might move forward on this question. We first outline the conditions under which biotic interactions can result in range shifts that proceed faster or slower than climate velocity and result in ecological surprises. Next, we use our own work to demonstrate that experimental studies documenting the strength of biotic interactions across large environmental gradients are a critical first step for understanding whether they will influence climate change–induced range shifts. Further progress could be made by integrating results from these studies into modeling frameworks to predict how or generalize when biotic interactions mediate how changing climates influence range shifts. Finally, we argue that many more case studies like those described here are needed to explore the importance of biotic interactions during climate change–induced range shifts.

Biodiversity citizen science projects are growing in number, size, and scope, and are gaining recognition as valuable data sources that build public engagement. Yet publication rates indicate that citizen science is still infrequently used as a primary tool for conservation research and the causes of this apparent disconnect have not been quantitatively evaluated. To uncover the barriers to the use of citizen science as a research tool, we surveyed professional biodiversity scientists (n = 423) and citizen science project managers (n = 125). We conducted three analyses using non-parametric recursive modeling (random forest), using questions that addressed: scientists' perceptions and preferences regarding citizen science, scientists' requirements for their own data, and the actual practices of citizen science projects. For all three analyses we identified the most important factors that influence the probability of publication using citizen science data. Four general barriers emerged: a narrow awareness among scientists of citizen science projects that match their needs; the fact that not all biodiversity science is well-suited for citizen science; inconsistency in data quality across citizen science projects; and bias among scientists for certain data sources (institutions and ages/education levels of data collectors). Notably, we find limited evidence to suggest a relationship between citizen science projects that satisfy scientists' biases and data quality or probability of publication. These results illuminate the need for greater visibility of citizen science practices with respect to the requirements of biodiversity science and show that addressing bias among scientists could improve application of citizen science in conservation.

Students from underrepresented groups start college with the same level of interest in STEM majors as their peers, but leave STEM at higher rates. We tested the hypothesis that low grades in general chemistry contribute to this “weeding,” using records from 25,768 students. In the first course of a general chemistry series, grade gaps based on binary gender, race/ethnicity, socioeconomic status, and family education background ranged from 0.12 to 0.54 on a four-point scale. Gaps persisted when the analysis controlled for academic preparation, indicating that students from underrepresented groups underperformed relative to their capability. Underrepresented students were less likely than well-represented peers to persist in chemistry if they performed below a C−, but more likely to persist if they got a C or better. This “hyperpersistent zone” suggests that reducing achievement gaps could have a disproportionately large impact on efforts to achieve equity in STEM majors and professions.

Active learning in college classes and participation in the workforce frequently hinge on small group work. However, group dynamics vary, ranging from equitable collaboration to dysfunctional groups dominated by one individual. To explore how group dynamics impact student learning, we asked students in a large-enrollment university biology class to self-report their experience during in-class group work. Specifically, we asked students whether there was a friend in their group, whether they were comfortable in their group, and whether someone dominated their group. Surveys were administered after students participated in two different types of intentionally constructed group activities: 1) a loosely-structured activity wherein students worked together for an entire class period (termed the ‘single-group’ activity), or 2) a highly-structured ‘jigsaw’ activity wherein students first independently mastered different subtopics, then formed new groups to peer-teach their respective subtopics. We measured content mastery by the change in score on identical pre-/post-tests. We then investigated whether activity type or student demographics predicted the likelihood of reporting working with a dominator, being comfortable in their group, or working with a friend. We found that students who more strongly agreed that they worked with a dominator were 17.8% less likely to answer an additional question correct on the 8-question post-test. Similarly, when students were comfortable in their group, content mastery increased by 27.5%. Working with a friend was the single biggest predictor of student comfort, although working with a friend did not impact performance. Finally, we found that students were 67% less likely to agree that someone dominated their group during the jigsaw activities than during the single group activities. We conclude that group activities that rely on positive interdependence, and include turn-taking and have explicit prompts for students to explain their reasoning, such as our jigsaw, can help reduce the negative impact of inequitable groups.

This paper describes the development and validation of a survey to measure students’ self-reported engagement during a wide variety of in-class active-learning exercises. The survey provides researchers and instructors alike with a tool to rapidly evaluate different active-learning strategies from the perspective of the learner.

Discipline-based education researchers have a natural laboratory—classrooms, programs, colleges, and universities. Studies that administer treatments to multiple sections, in multiple years, or at multiple institutions are particularly compelling for two reasons: first, the sample sizes increase, and second, the implementation of the treatments can be intentionally designed and carefully monitored, potentially negating the need for additional control variables. However, when studies are implemented in this way, the observations on students are not completely independent; rather, students are clustered in sections, terms, years, or other factors. Here, I demonstrate why this clustering can be problematic in regression analysis. Fortunately, nonindependence of sampling can often be accounted for with random effects in multilevel regression models. Using several examples, including an extended example with R code, this paper illustrates why and how to implement random effects in multilevel modeling. It also provides resources to promote implementation of analyses that control for the nonindependence inherent in many quasi-random sampling designs.