We Can't Fail Again: Arguments for Professional Development in the Wake of COVID-19.

The majority of academic institutions were underprepared for a global pandemic, leading to spikes in instructor anxiety and drops in student engagement with STEM courses. With many STEM professors teaching online for the first time, they independently sought out training in distance education and inclusive teaching practices. Had institutions been proactive in providing such professional development prior to the pandemic, the negative impacts of transitioning to online education would have been reduced. While recent events are still fresh in people's minds, we advocate for increased or maintained professional development opportunities for STEM instructors in order to protect this critical pedagogical support from budget cuts.

The College Science Learning Cycle: An Instructional Model for Reformed Teaching.

Finding the time for developing or locating new class materials is one of the biggest barriers for instructors reforming their teaching approaches. Even instructors who have taken part in training workshops may feel overwhelmed by the task of transforming passive lecture content to engaging learning activities. Learning cycles have been instrumental in helping K-12 science teachers design effective instruction for decades. This paper introduces the College Science Learning Cycle adapted from the popular Biological Sciences Curriculum Study 5E to help science, technology, engineering, and mathematics faculty develop course materials to support active, student-centered teaching approaches in their classrooms. The learning cycle is embedded in backward design, a learning outcomes-oriented instructional design approach, and is accompanied by resources and examples to help faculty transform their teaching in a time-efficient manner.

Multiplatform single-sample estimates of transcriptional activation.

Over the past two decades, many biotechnology platforms have been developed for high-throughput gene expression profiling. However, because each platform is subject to technology-specific biases and produces distinct raw-data distributions, researchers have experienced difficulty in integrating data across platforms. Data integration is crucial to data-generating consortiums, researchers transitioning to newer profiling technologies, and individuals seeking to aggregate data across experiments. We address this need with our Universal exPression Code (UPC) approach, which corrects for platform-specific background noise using models that account for the genomic base composition and length of target regions; this approach also uses a mixture model to estimate whether a gene is active in a particular profiling sample. The latter produces standardized UPC values on a zero-to-one scale, so that they can be interpreted consistently, irrespective of profiling technology, thus enabling downstream analysis pipelines to be developed in a platform-agnostic manner. The UPC method can be applied to one- and two-channel expression microarrays and to next-generation sequencing data (RNA sequencing). Furthermore, UPCs are derived using information from within a given sample only--no ancillary samples are required at processing time. Thus, UPCs are suitable for personalized-medicine workflows where samples must be processed individually rather than in batches. In a variety of analyses and comparisons, UPCs perform comparably to other methods designed specifically for microarrays or RNA sequencing in most settings. Software for calculating UPCs is freely available at www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html.

Collaborative testing improves performance but not content retention in a large-enrollment introductory biology class.

Collaborative testing has been shown to improve performance but not always content retention. In this study, we investigated whether collaborative testing could improve both performance and content retention in a large, introductory biology course. Students were semirandomly divided into two groups based on their performances on exam 1. Each group contained equal numbers of students scoring in each grade category ("A"-"F") on exam 1. All students completed each of the four exams of the semester as individuals. For exam 2, one group took the exam a second time in small groups immediately following the individually administered test. The other group followed this same format for exam 3. Individual and group exam scores were compared to determine differences in performance. All but exam 1 contained a subset of cumulative questions from the previous exam. Performances on the cumulative questions for exams 3 and 4 were compared for the two groups to determine whether there were significant differences in content retention. Even though group test scores were significantly higher than individual test scores, students who participated in collaborative testing performed no differently on cumulative questions than students who took the previous exam as individuals.