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).
翻译:由于对数据科学教育的兴趣前所未有而且日益浓厚,教育材料有限,为学习者提供了有意义的机会来进行统计思维,如Wild和Pfannkuch(1999年)所界定的那样,教育材料有限,而数据混乱,数据混乱,解决了现实世界的挑战,Nolan和Speed(1999年)主张将本科本科统计课程的应用放在最前沿,利用深入的案例研究鼓励和开发课堂上的统计思维,这一方法的局限性包括进行案例研究所需的大量时间投资 -- -- 即选择一个激励性的问题和进行说明性数据分析 -- -- 以及所需的领域专门知识。结果,基于现实挑战而不是玩具实例的案例研究十分稀少。为了解决这个问题,我们制定了开放案例研究项目(http://www.opencastudies.org),该项目提供了一个新的统计和数据科学教育案例研究模型,鼓励和发展课堂上的统计和科学思维。这一教育资源提供了自成一体、多式、同行审查和开放源指南(或案例研究),用于全面数据分析的积极经验。我们编制了一个教育工作者指南,说明如何最有效地利用案例研究,如何修改和调整新的案例研究的组成部分。