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Littlefield, C. E.

Publications and source records attributed to Littlefield, C. E..

2 recordsLinked to original sources

Active learnings impact on student course performance in STEM varies by type and intensity

We updated a recent meta-analysis of active learnings impact on student achievement in undergraduate STEM courses by following the same protocol to evaluate studies published from 2010-2017. We screened 1659 papers, coded 1294, and found 210 that met five pre-established inclusion criteria and six pre-established criteria for methodological quality. After further dropping 76 studies with no exam scores data, 134 of these studies contained data on student performance on identical or equivalent exams. We found that on average, active learnings effect size on exam scores was 0.519 {+/-} 0.049, meaning that when students are in active learning classes, they perform roughly half a standard deviation higher on an identical exam. Funnel plots and sensitivity analyses indicated that these results were not due to sampling bias. Active learning had a positive impact on student outcomes regardless of class size, course level, or STEM discipline, though there was heterogeneity in the effects. All of these results are very similar when compared to earlier meta-analyses, however increased resolution in the studies analyzed here revealed two novel results. First, student performance was significantly better in courses that employed high-intensity active learning, defined as students being on task at least two-thirds of class time, versus lower-intensities. Additionally, there was significant heterogeneity in efficacy across different types of active learning employed. These results suggest that most, if not all types of active learning are effective, and that when innovating in their classes, instructors should continually work to increase active learning intensity. We urge caution in interpreting the results on active learning types, however, and propose a preliminary framework for making more-sophisticated and reliable analyses of variation in course design. Finally, the evidence presented here for active learnings impact on student outcomes creates a strong foundation for faculty professional development and administration.

scientific communication and education↗

Mapping connectivity and conservation opportunity on agricultural lands across the conterminous United States

O_LIDepending on management practices, agricultural lands can either pose substantial barriers to the movement of native species or can support landscape connectivity by linking areas of high-quality habitat. Balancing connectivity and sustainable food production on agricultural lands is critical to conservation in the conterminous United States (CONUS) where agriculture makes up close to half of total land area. However, limited guidance exists on where to target conservation resources to maximize benefits for native species and food security. C_LIO_LITo quantify the potential contribution of agricultural lands to the movement of organisms, we developed a novel method for estimating agricultural management intensity (based on remotely sensed temporal variation in vegetation cover on croplands and pastures) and incorporated these estimates into a CONUS-wide, circuit-theory based model of ecological flow connectivity. We then combined our connectivity results with data on the productivity, versatility, and resilience of agricultural lands (PVR) to identify conservation opportunities that support both biodiversity and food production. C_LIO_LIThe highest levels of connectivity on agricultural lands occurred on relatively unmodified rangelands and on cropland and pasture in close proximity to large amounts of natural land cover. C_LIO_LIMapping connectivity and PVR across CONUS revealed 10.2 Mha of agricultural lands (2.7%) with high value for both connectivity and food production, as well as large amounts of agricultural land (>140 Mha in total) with high value for either cultivation or supporting biodiversity (e.g., through ecological restoration). C_LIO_LIDrawing on these findings, we provide recommendations on the types of conservation approaches most suitable for a given agricultural system and link these recommendations to specific government incentive programs. To help facilitate conservation planning based on our results, we have developed an interactive web application, allowing users to visualize the spatial data developed here within their regions of interest. C_LI

ecology↗