A Data-Driven, Multidisciplinary Curriculum Providing Access to the Data Analytics Economy through Project-based Learning

大多数薪酬最高、需求旺盛的工作现在都需要数据分析技能，这使得数据分析和解释技能可以说与阅读或写作一样重要。该项目旨在让未来的STEM劳动力具备推动跨行业创新所需的数据分析技能。为此，该项目将传播基于项目的数据分析课程，使学生能够在学生自己的研究项目的背景下，使用领先的分析平台(例如，SAS；R；Python；Stata)来探索和解释大数据。本课程旨在帮助学生体验数据驱动的查询的力量和兴奋感，而不考虑他们的准备或最初的兴趣。该项目的目的是在不同的教育环境中实施这一课程，并培训教育工作者，使课程能够接触到大量的学习者。据项目估计，这一实施将直接涉及全国70多个教育机构、数百名教师和数千名学生。通过让数据科学教育更容易获得，该项目旨在增加招聘和留住女性和其他代表性不足的学生，进入需要数据分析技能的职业。通过这种方式，它可以帮助创造一个更大、更多样化的人口，拥有跨行业、学科和受众所需的数据分析技能，从而为国家在全球经济中的竞争力做出贡献。该计划旨在利用新实施地点的现有基础设施，并整合一套协调的循证实践，以支持学生和教师使用大数据集学习和参与研究项目。该项目的主要特点包括：1)与学习者的兴趣和内在动机挂钩的基于项目的学习；2)多学科探究的机会；3)在现实世界的背景下分析大数据集；4)编程作为数据驱动的推理和交流的窗口；以及5)密集的、以学生为中心的一对一支持，利用基于证据的战略来促进代表性不足的青年的成功。该项目将使用事前/事后调查，准实验设计，使用最先进的因果推理技术，以及机构数据，来回答五个研究问题：课程是否会带来积极的学生结果？该课程是否增加了妇女、代表性不足的学生和有学习障碍的学生接触数据分析技能的机会？教育者培训和专业发展的模式在培养知识和对其交付的信心方面是否有效？参与的教育工作者在多大程度上应用和维持基于项目的模式在他们的计划和课堂中？在机构层面，什么样的支持形式和财政模式为数据驱动型课程提供了最大的可持续性？通过开展包括教育者培训、项目可持续性和学生成果等领域的评价性研究，该项目将贡献关于教与学数据分析的新知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Most of the highest paying, in-demand jobs now require skills in data analysis, making data analysis and interpretation skills arguably as important as reading or writing. This project aims to equip the future STEM workforce with the data analysis skills needed to advance innovation across industries. To this end, this project will disseminate a project-based data analysis curriculum that enables students to use leading analytic platforms (e.g., SAS; R; Python; Stata) to explore and interpret big data, in the context of students' own research projects. This curriculum is designed to help students experience the power and excitement of data-driven inquiry, regardless of their preparation or initial interest. The project aims to implement this curriculum in varied educational settings and to train educators so that the curriculum can reach large numbers of learners. By project estimates, this implementation will directly involve more than 70 educational settings across the country, hundreds of instructors, and thousands of students. By making data science education more accessible, the project aims to increase the recruitment and retention of women and other underrepresented students, into careers requiring data analysis skills. In this way, it can help to create a larger, more diverse population with the data analysis skills needed across industry sectors, disciplines, and audiences, thus contributing to the nation's competitiveness in the global economy. The program is designed to leverage existing infrastructure at new implementation sites and integrate a coordinated set of evidence-based practices to support students' and instructors' learning and engagement in research projects with large data sets. Key characteristics of the project include: 1) project-based learning tied to learner interest and intrinsic motivation; 2) opportunities for multidisciplinary inquiry; 3) analysis of large data sets in real world contexts; 4) programming as a window into data-driven reasoning and communication; and 5) intensive, student-centered one-on-one support that capitalizes on evidence-based strategies to promote success for underrepresented youth. The project will use a pre/post survey, quasi-experimental design, employing state-of-the-art causal inference techniques, together with institutional data, to answer five research questions: Does the curriculum result in positive student outcomes? Does the curriculum increase exposure to data analysis skills for women, under-represented students, and students with learning disabilities? Is the model of educator training and professional development effective in fostering knowledge and confidence in its delivery? To what extent do participating educators apply and sustain the project-based model within their programs and classrooms? At the institutional level, what format and fiscal model of support provides greatest sustainability for the data-driven curriculum? By conducting evaluative research that includes the areas of educator training, program sustainability, and student outcomes, the project will contribute new knowledge about teaching and learning data analytics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.