Advancing the Science of Learning Data Science with Adaptive Learning for Future Workforce Development

该项目旨在通过改进数据科学培训来服务于国家利益。数据科学家需要为正在进行的大数据革命提供动力，这场革命正在改变经济的几乎每个部门。目前，由于缺乏对如何学习数据科学的理解，以及缺乏优化这种学习的技术，在培训数据科学家方面的进展有限。这个项目将通过将统计、编程、机器学习和关于学生学习的实验结果编织在一起，促进对数据科学如何学习的理解。它将利用这一理解创建一个创新的、支持人工智能的数据科学导师，名为“DataWhys”。DataWhys导师可以集成到JupyterLab，这是一个成熟的专业数据科学工具，将提供250小时的培训内容。为了促进对如何学习数据科学以及如何优化这种学习的理解，该项目将为不同专业水平的工作示例确定最有效的支架，并确定何时应该拆除支架。然后，它将把实施这些发现的数据科学智能辅导条件与工作样本和纯粹的问题解决控制进行比较。这种方法将综合统计、编程和机器学习教育等相关领域的先前工作，每个领域都只使用了本项目将全面研究的少数几个支架和技术。除了对大学新生、STEM专业的学生和研究生进行横断面研究外，还将与圣裘德儿童研究医院的数据科学部合作，并通过勒莫因-欧文学院STEM专业的暑期实习来进行纵向研究。这些纵向研究将通过可用性指标和个人学习计划的进展提供关于劳动力相关性的额外证据。根据该项目制作的源代码和培训材料将在GitHub上公开共享，任何人都可以在那里自由使用和修改，使用的是开源的阿帕奇许可证。该项目得到了加速发现：教育未来STEM劳动力计划的支持，该计划为在NSF投资的大想法定义的关键科学领域教育STEM劳动力的项目提供资金。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。

This project aims to serve the national interest by improving training in data science. Data scientists are needed to power the ongoing revolution in Big Data that is transforming virtually every sector of the economy. Progress in training data scientists is currently limited by a lack of understanding about how data science is learned and by a lack of techniques to optimize that learning. This project will advance understanding of how data science is learned by weaving together statistics, programming, and machine learning and experimental results about student learning. It will use this understanding to create an innovative Artificial Intelligence-enabled data science tutor called “DataWhys.” The DataWhys tutor can be integrated into JupyterLab, an established professional data science tool, and will provide 250 hours of training content.To advance understanding of how data science is learned and how to optimize that learning, this project will identify the most effective scaffolds for worked examples across varying levels of expertise and identify when scaffolds should be removed. It will then compare a data science intelligent tutoring condition that implements these findings against worked example and pure problem-solving controls. This approach will synthesize previous work in the related fields of statistics, programming, and machine learning education, each of which has used only a few of the scaffolds and techniques that will be comprehensively investigated in this project. In addition to cross-sectional studies with college freshman, STEM majors, and graduate students, longitudinal studies will be conducted in partnership with the data science division of St. Jude Children's Research Hospital and through a summer internship for STEM majors from LeMoyne-Owen College. These longitudinal studies will provide additional evidence regarding workforce relevance through usability metrics and progress in personal learning plans. Source code and training material produced under the project will be publicly shared on GitHub where it can be freely used and modified by anyone under the open-source Apache license. This project is supported by the Accelerating Discovery: Educating the Future STEM Workforce program, which funds projects to educate the STEM workforce in the critical scientific areas defined by the Big Ideas for NSF Investment.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.