Abstract Content (up to 300 words recommended)
The ‘-omics’ revolution has created a vast, complex, and interlinked network of data that describes how cells and organisms work at a systems level. It is imperative for a modern science curriculum to include systems biology, as it now constitutes a key part of applied and basic research in many fields of work.
Despite this, systems biology is often only presented to students in lectures as an abstract or computational entity, without associated hands-on practicals. When practicals are offered, students are regularly asked to work on simplified, simulated, or pre-analyzed datasets because real data sets are complex and confusing. As a result, students can lack experience in analyzing complex data and reconstructing its (biological) meaning; these skills, however, are key in modern industry and research workplaces and we do our students a disservice by avoiding complexity.
To tackle this educational problem we introduced a new nine-week practical into a third-level Systems Biology course taught at the University of Queensland. The practical draws directly on a current research project in the lead author’s laboratory, and students generate and analyze their own proteomic data. Students choose both the method of sample preparation and the focal point of their investigations (i.e. a particular metabolic process, or a group of enzymes), which guides their in-depth data analysis.
Students voluntarily participated in a pre-/post-practical survey. Our innovation led to a significant improvement in the course evaluation. Students indicated that conducting a research project in order to learn actual, workplace-relevant skills was a great plus. In addition, lectures and practicals were seen as reinforcing one another, leading to student-reported improved learning outcomes. We will present (i) a full analysis of the student-generated data; (ii) a description of the research project itself; (iii) the lead author’s journey as a new designer and implementer of ALURE.
Addressing the theme/s of the Conference (up to 200 words recommended)
-->How does this abstract fit the conference theme “Learning for Life and Work in a complex world” – Navigating uncertainty and complexity?
In almost any modern scientific workplace there is a demand for increasingly complex data organisation, assessment, and analysis. This complexity and volume of data is a direct result of continually improving methods of analysis. The intervention described above addressed this topic by teaching students fundamental principles of complex data structuring and analysis. We were able to ask our students to generate, order, and find meaning in a large, uncharted data set. These key skills are essential if students are to avoid being overwhelmed by complex data or analyses at hand.