  With unprecedented and growing interest in data science education, there are
limited educator materials that provide meaningful opportunities for learners
to practice statistical thinking, as defined by Wild and Pfannkuch (1999), with
messy data addressing real-world challenges. As a solution, Nolan and Speed
(1999) advocated for bringing applications to the forefront in undergraduate
statistics curriculum with the use of in-depth case studies to encourage and
develop statistical thinking in the classroom. Limitations to this approach
include the significant time investment required to develop a case study --
namely, to select a motivating question and to create an illustrative data
analysis -- and the domain expertise needed. As a result, case studies based on
realistic challenges, not toy examples, are scarce. To address this, we
developed the Open Case Studies (https://www.opencasestudies.org) project,
which offers a new statistical and data science education case study model.
This educational resource provides self-contained, multimodal, peer-reviewed,
and open-source guides (or case studies) from real-world examples for active
experiences of complete data analyses. We developed an educator's guide
describing how to most effectively use the case studies, how to modify and
adapt components of the case studies in the classroom, and how to contribute
new case studies. (https://www.opencasestudies.org/OCS_Guide).
