Evaluating the Representation of Community Colleges in Biology Education Research Publications following a Call to Action.

Interest in biology education research (BER) has been growing over the last two decades, yet few BER publications focus on community colleges, which serve a large percentage of the undergraduate student population and a majority of those students who identify with historically underserved groups. In this paper, we define community college biology education research (CC BER) as publications with a community college faculty member as an author, publications with a community college study context or a focus on community college biology teaching and learning, and publications that use community college students as a source of data. We conducted a literature review to quantify how CC BER has progressed since initial calls for broadening participation by recording the number of CC BER publications in seven prominent journals between 2016 and 2020. Our formal analysis of peer-reviewed BER literature indicates that there has been a statistically significant increase in CC BER publications from 3.2% to 5.9% of total BER publications since the last analysis in 2017. We conclude with a discussion of strategies for further broadening of participation in CC BER.

Meta-analysis of Gender Performance Gaps in Undergraduate Natural Science Courses.

To investigate patterns of gender-based performance gaps, we conducted a meta-analysis of published studies and unpublished data collected across 169 undergraduate biology and chemistry courses. While we did not detect an overall gender gap in performance, heterogeneity analyses suggested further analysis was warranted, so we investigated whether attributes of the learning environment impacted performance disparities on the basis of gender. Several factors moderated performance differences, including class size, assessment type, and pedagogy. Specifically, we found evidence that larger classes, reliance on exams, and undisrupted, traditional lecture were associated with lower grades for women. We discuss our results in the context of natural science courses and conclude by making recommendations for instructional practices and future research to promote gender equity.

Teaching during COVID-19 Times: A Community College Perspective.

In March 2020, the COVID-19 pandemic altered instructional and learning strategies at institutions across the globe. This emergency transition to remote instruction (ETRI) resulted in ambiguity regarding what to teach, how to teach, and instructor and student workload. We report on a survey-based study of 44 community college (CC) faculty at 16 institutions, with the aim of documenting how our CC faculty colleagues perceived the ETRI, the challenges they faced, and the resources that were-or would have been-most helpful. We conclude with recommendations, in the words of participating faculty, to address prevailing concerns voiced by these instructors: namely, the lack of student-faculty interactions in the online space, concerns about student access to resources, and the demand for authentic research and lab experiences.

DIY ecology class: Transitioning field activities to an online format.

The COVID-19 pandemic has forced the transition of many traditional face-to-face classes into an online format with little time to prepare best practice guidelines. In this article, we share a case study of how we adapted a group field activity into an individual laboratory assignment that can be completed during shelter-in-place restrictions. We conclude with ideas for future applications while staying mindful of the ways in which this pandemic has highlighted the inequities of the classroom, especially at community colleges.

A Call for Data-Driven Networks to Address Equity in the Context of Undergraduate Biology.

National efforts to improve equitable teaching practices in biology education have led to an increase in research on the barriers to student participation and performance, as well as solutions for overcoming these barriers. Fewer studies have examined the extent to which the resulting data trends and effective strategies are generalizable across multiple contexts or are specific to individual classrooms, institutions, or geographic regions. To address gaps in our understanding, as well as to establish baseline information about students across contexts, a working group associated with a research coordination network (Equity and Diversity in Undergraduate STEM, EDU-STEM) convened in Las Vegas, Nevada, in November of 2019. We addressed the following objectives: 1) characterize the present state of equity and diversity in undergraduate biology education research; 2) address the value of a network of educators focused on science, technology, engineering, and mathematics equity; 3) summarize the status of data collection and results; 4) identify and prioritize questions and interventions for future collaboration; and 5) construct a recruitment plan that will further the efforts of the EDU-STEM research coordination network. The report that follows is a summary of the conclusions and future directions from our discussion.

Investigating Instructor Talk in Novel Contexts: Widespread Use, Unexpected Categories, and an Emergent Sampling Strategy.

Instructor Talk-noncontent language used by instructors in classrooms-is a recently defined and promising variable for better understanding classroom dynamics. Having previously characterized the Instructor Talk framework within the context of a single course, we present here our results surrounding the applicability of the Instructor Talk framework to noncontent language used by instructors in novel course contexts. We analyzed Instructor Talk in eight additional biology courses in their entirety and in 61 biology courses using an emergent sampling strategy. We observed widespread use of Instructor Talk with variation in the amount and category type used. The vast majority of Instructor Talk could be characterized using the originally published Instructor Talk framework, suggesting the robustness of this framework. Additionally, a new form of Instructor Talk-Negatively Phrased Instructor Talk, language that may discourage students or distract from the learning process-was detected in these novel course contexts. Finally, the emergent sampling strategy described here may allow investigation of Instructor Talk in even larger numbers of courses across institutions and disciplines. Given its widespread use, potential influence on students in learning environments, and ability to be sampled, Instructor Talk may be a key variable to consider in future research on teaching and learning in higher education.

Classroom sound can be used to classify teaching practices in college science courses.

Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.