Authentic ecological data as a tool to promote engagement and academic success among virtual biology students

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Alexis A. Diaz, Chloe Van Grootheest and Raisa Hernández Pacheco, Biology, Cal State University Long Beach, Long Beach, CA

Background/Question/Methods
College connectedness is a crucial element of student engagement and academic success. However, the current global health situation has pushed most academic activities to virtual settings, fragmenting the bridge of student connectedness, and thus potentially decreasing academic success. This is especially challenging for quantitative ecology students that are in growing need of activities that both meet learning outcomes and increase engagement in a virtual setting. One solution for designing virtual activities that promote engagement and academic success may rely on the use of data-centered activities bridging back to campus. Here, we implemented authentic ecological research data, generated by multiple research laboratories in our Biological Sciences department, into data-centered activities in a biostatistics course. We anticipated that by featuring professors from their own campus community, students would be able to experience virtual engagement with their institution and regain college connectedness. The main goals for our designed activities were to (1) provide direct biological content to each teaching module by using peer-generated authentic research data, (2) introduce R programming language as the data management and statistical tool, and (3) provide fully electronic tools for teaching that instructors can further adapt to their own student community.
Results/Conclusions
The many tools developed so far to manage and analyze ecological data using R programming language are vast and easily adaptable to the virtual classroom with high potential for increased engagement. Students that took our virtual course using their peers ecological research data said they felt engaged with the course and agreed that the data-centered activities increased their appreciation for biostatistics. By using data generated by the same laboratories that students contribute to as members during their degree, we can improve student sense of integration and belonging with their university that will feedback into academic success in traditionally challenging quantitative biology courses. This model is easily adaptable to many academic settings through free educational hubs, where educators can adapt the materials for their own implementation.