Google is our Friend: In-class data sharing in large cohort practicals

Making practical classes interesting and engaging is a constant challenge in undergraduate science programmes. In the first year of study, practical classes often consist of following a set protocol that will deliver a known, fixed end result. While this teaches students important basic practical skills and aspects of data collection, it does not engage students in the process of scientific investigation. In addition, today’s science degrees must prepare students for many different future career paths, not just for a career in science. Hence the role of the practical class must evolve to also develop transferable skills relevant to the modern world.

To expand the range of skills experienced early in their undergraduate careers, we have developed large practical workshops that enable 100 first year neuroscience students to perform their own experiments with a large element of uncertainty about what the results might show. We have used a database of neuronal morphology (http://www.neuromorpho.org) to allow the students to perform digital experiments, and Google apps to allow the data to be shared live during the class.

The Neuromorpho database is a curated inventory of over 60,000 digitally reconstructed neurons from across the brain that are associated with peer-reviewed publications. Each reconstructed neuron can be viewed in three dimensions with a 3D viewer and comes complete with links to the publication from which it is derived and a series of common measurements. Students are asked to randomly choose cells from selected brain regions that perform increasingly complex tasks. They note the measurements for a number of different neuronal characteristics, such as length of dendrites, number of dendritic branches and interbranch interval, as a measure of neuronal complexity. They are asked to compare their measurements across the brain regions to determine if there is any relationship between any of the measurements and the complexity of the tasks those neurons perform. Students then use Google apps to upload their data to a shared spreadsheet where it is automatically collated. Comparison of their own individual results to those derived from the class as a whole allows discussion of small versus large sample sizes and reliability of results.

The use of shared in-class data generated during practical sessions is applicable to many different teaching scenarios. Anecdotal feedback from has been generally positive about these sessions and we are evaluating the impact on students’ understanding of variability and uncertainly in science with a view to expanding its use in future.