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Prioritizing your Data Science Curriculum

Author(s): Lou Gross1, Sondra Marie LoRe2

1. University of Tennessee Knoxville 2. National Institute for STEM Evaluation & Research (NISER)

465 total view(s), 113 download(s)

Summary:

This is the summary powerpoint of the Monday session at the BioQUEST / QUBES 2019 Summer Workshop including the description of the student data project on height change overnight.

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 29 Jul 2019 doi:10.25334/3HWQ-3024 - cite this

Description

A central issue in undergraduate education is how to effectively prioritize the vast set of concepts and skills that might be included in a curriculum. This is particularly an issue in areas such as data science that cross many quantitative and domain disciplines. Prioritization typically arises from institutional history, recommendations from national reports, accreditation standards, etc. This session will focus on an alternative rational method to constrain the potentially immense collection of quantitative training that might be incorporated in an undergraduate life science curriculum. The method is being piloted for graduate biomedical education through support from the Burroughs Wellcome Fund, but a modified methodology is appropriate at the undergraduate level. A key objective is to account for the constraints on faculty expertise at an institution, thus tailoring the curriculum based on local interests and expectations of and for students. This session will provide participants with a quantitative curriculum development rubric to combine faculty expertise at they own institution with guidance from national reports, such as the areas included in the definition of “data acumen” in the National Academies report “Envisioning the Data Science Discipline: The Undergraduate Perspective”. 

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