Optimising the learning experience in programming courses for researchers at any level

Mr Malcolm Ramsay1, Dr Anastasios Papaioannou1, Mr Aidan Wilson1,2, Dr Weisi Chen1,3

1Intersect, , Australia
2Australian Catholic University, , Australia
3University of Technology Sydney, Australia

Since Intersect incorporated programming languages into our researcher training catalogue, we have seen a continuing and rapid increase in demand for courses in Python, R, MATLAB and Julia. In 2019, we trained more than 2100 researchers in programming courses alone, using the Carpentries material as a basis and complemented by Intersect’s own courses.

We notice there are three types of researchers attending our courses; those completely new to programming and hesitating about which language to use, those with programming experience but relatively new to a particular language they want to use, and those with experience in a language who want to extend their skills into libraries for data analysis and visualisation. To address this, we have established teaching pathways optimising the learning process of researchers at a level they are comfortable with and matching their skills.

At a foundational level, a new series of awareness-raising webinars introduces learners to programming concepts in preparation for interactive learning. Introductory units focus on exemplifying these concepts rather than teaching a particular language, preparing learners for any language. Finally, more advanced units build on this foundation, allowing more advanced learners to extend their skills for specific data analysis use-cases based on their research needs.

In this presentation, we explain the motivations for developing these new teaching pathways, including an analysis of course evaluations and open-ended feedback. We discuss how the Carpentries materials fit into a broader curriculum, bookended by language-agnostic awareness and introductory courses, and use-case specific advanced courses.


Malcolm Ramsay is the eResearch Training Administrator at Intersect where he works to improve all aspects of training and raises awareness of digital tools within the research community. During his PhD, Malcolm used computer simulations to understand the role of shape in crystal melting, incorporating Data Science tools to the academic environment. Malcolm’s industry experience comes from Co-Founding the startup FluroSat, where he applied his data analysis expertise understanding crop stresses using remote sensing. When Malcolm isn’t working to understand and communicate data, he gets as far away from his computer as possible; running, cycling, or climbing a rock face.

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