bioST@TS, a Learning Platform for Statistical Analysis and Management of Biological Data

Jonathan Soulé, Øystein Varpe, Sigrunn Eliassen

Biology is a discipline that makes extensive use of mathematical models, numerical tools, data management, and statistical analysis. In the course of their curriculum, biology students must acquire numerical skills and quantitative competence to better comprehend biological theories, systems and problems (‘Vision and Change’; AAAS 2011). However, many students do not appear to successfully translate these skills into their subject context. In the classroom, educators face the challenge to keep their audience engaged and confident when trying to apply quantitative reasoning. Even if courses in mathematics and statistical analysis are compulsory in the curriculum, they either seem maladapted to biological problems, or fail to put numerical knowledge into the biological context (Touchon et al., 2016). Most higher-education institutions also lack a concrete plan for giving students and teachers the tools to make numeracy a transferable skill in courses and study programs (Speth et al. 2010).

The Centre for Excellence in Biology Education, bioCEED has created bioST@TS, a web-based learning platform ( dedicated to helping biology students understand the basics of data management and statistical analysis. Directed towards both bachelor- and master students, bioST@TS provides tutorials and instructive videos that are relevant primarily, but not exclusively, for biology courses at University of Bergen (Norway) and at the University Centre in Svalbard (Norway). The platform makes broad use of videos since this media has been found to increase student achievement, competence, learner satisfaction and engagement (Dupuis et al., 2013; Oruset al., 2016; Sherer & Shea, 2011). A pilot study suggests that bioST@TS video resources constitute an effective tool as a supplement to regular teaching.

bioST@TS learning modules for undergraduate students focus on the basics of data management and visualization through tables and charts in MS Excel 2016. Modules for master students include statistical analysis and apply the open source programme R, with instructions to the coding needed in this program. bioST@TS also offers videos that explain key-concepts in statistics using simple, concrete examples in biology. bioST@TS is also a repository for resources created in collaboration with both teachers and students.

This poster will provide an overview of the modules and resources available on the website, as well as some reflections on the scholarly motivation behind the initiative and experiences with how it so far has helped promote learning and understanding of biological phenomenon. Tablets will be available for participants to practically explore the platform.

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