Opinionated practices for teaching reproducibility: motivation, guided instruction and practice

TL;DR

This paper discusses effective strategies for teaching reproducibility in data science courses, emphasizing motivation, guided instruction, and practice to overcome learning challenges.

Contribution

It introduces practical teaching methods that enhance student engagement and understanding of reproducibility in data science education.

Findings

Motivation increases student interest in reproducibility.

Guided instruction improves learning outcomes.

Ample practice solidifies understanding.

Abstract

In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modelling, which is often one of the most interesting topic to novices. This fact, along with the highly technical nature of the industry standard reproducibility tools currently employed in data science, present out-of-the gate challenges in teaching reproducibility in the data science classroom. Put simply, students are not as intrinsically motivated to learn this topic, and it is not an easy one for them to learn. What can a data science educator do? Over several iterations of teaching courses focused on reproducible data science tools and workflows, we have found that providing extra motivation, guided instruction and lots of practice are key to effectively teaching this challenging, yet important subject. Here we present examples of how we deeply motivate, effectively guide and provide ample practice opportunities to data science students to effectively engage them in learning about this topic.