Peter Kedron, UC Santa Barbara; Joseph Holler, Middlebury College; Andrew Trgovac, Arizona State University
A consensus is emerging across the sciences that the reproducibility of research must be improved and that more independent reproductions and replications are needed to assess the state of existing knowledge. A similar view is emerging among GIScientists and spatial data scientists. Multiple authors have called for improvements in the transparency and accessibility of procedures and data, the adoption of open science practices, and the execution of reproduction and replication studies. However, a parallel consensus has yet to develop about how to teach reproducibility in spatial data science. Educators have no competency model to use to structure their curricula, no set of best practices to inform their class exercises, and few model courses to emulate at their own institutions.
In this workshop, we introduce a series of materials designed to help educators introduce reproducibility into their spatial data science courses and institutional curricula.
At the course scale, we discuss how to present reproduction and replication as dynamic processes of critical evaluation and scientific knowledge creation. We conceptually link reproduction attempts with the problem-based learning model, and provide an actionable framework educators can use to integrate R&R into a semester course. We also present how this framework can be scaled to fit different course levels and instructional modalities. Drawing on our experience attempting reproductions and replications and using R&R projects in the classroom, we discuss common challenges faced when implementing this pedagogical approach and the path to successfully publishing such studies.
At the curricular scale, we introduce a competency rubric for reproducibility that outlines essential skills students should acquire and levels of achievement to measure success. We also highlight a recently developed evaluation tool to help educators assess their existing materials. Finally, we highlight some of our own ongoing projects and invite those interested to join our wider effort to enhance the reproducibility of spatial data science.
The session organizers