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

通过适应性学习促进未来劳动力发展的学习数据科学科学

• 批准号: 1918751
• 负责人: Andrew Olney
• 金额: $ 343.9万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918751&HistoricalAwards=false
• 关键词: Advancing; Science; Learning; Data; Adaptive

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.

该项目旨在通过改善数据科学培训来服务于国家利益。需要数据科学家来推动正在进行的大数据革命，这场革命几乎改变了经济的每个部门。目前，数据科学家培训的进展受到限制，因为缺乏对数据科学如何学习的理解，以及缺乏优化这种学习的技术。该项目将通过将统计、编程、机器学习和关于学生学习的实验结果编织在一起，来促进对数据科学如何学习的理解。 它将利用这种理解来创建一个创新的人工智能数据科学导师，称为“DataWhys”。DataWhys导师可以集成到一个成熟的专业数据科学工具QuixyterLab中，并将提供250小时的培训内容。为了进一步了解如何学习数据科学以及如何优化学习，该项目将为不同专业水平的工作示例确定最有效的支架，并确定何时应该删除支架。 然后，它将比较一个数据科学智能辅导条件，该条件将这些发现与工作示例和纯问题解决控制进行比较。这种方法将综合统计学，编程和机器学习教育相关领域的先前工作，其中每一个都只使用了一些支架和技术，这些支架和技术将在本项目中进行全面研究。除了与大学新生，STEM专业和研究生的横断面研究外，还将与圣裘德儿童研究医院的数据科学部门合作进行纵向研究，并通过LeMoyne-Owen学院STEM专业的暑期实习进行。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有关劳动力相关性的额外证据。在该项目下生成的源代码和培训材料将在GitHub上公开共享，任何人都可以在开源Apache许可证下自由使用和修改。 该项目由加速发现：教育未来的STEM劳动力计划支持，该计划资助在NSF投资大创意定义的关键科学领域教育STEM劳动力的项目。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Repeated Measures of Cognitive and Affective Learning Outcomes in Simulation Debriefing

模拟汇报中认知和情感学习成果的重复测量

• DOI: 10.1097/jte.0000000000000233
• 发表时间: 2022
• 期刊: Journal of Physical Therapy Education
• 作者: Tawfik, Andrew A.;Bradford, Jacque;Gish-Lieberman, Jaclyn;Gatewood, Jessica
• 通讯作者: Gatewood, Jessica

Comparing How Different Inquiry-based Approaches Impact Learning Outcomes

比较不同的探究式方法如何影响学习成果

• DOI: 10.14434/ijpbl.v14i1.28624
• 发表时间: 2020
• 期刊: Interdisciplinary Journal of Problem-Based Learning
• 影响因子: 1.2
• 作者: Tawfik, Andrew A;Hung, Woei;Giabbanelli, Philippe J.
• 通讯作者: Giabbanelli, Philippe J.

Designing for Self-Efficacy: E-Mentoring Training for Ethnic and Racial Minority Women in STEM

自我效能设计：针对 STEM 领域少数族裔和种族女性的电子辅导培训

• DOI: 10.14434/ijdl.v12i3.31433
• 发表时间: 2021
• 期刊: International Journal of Designs for Learning
• 作者: Gish-Lieberman, Jaclyn Joy;Rockinson-Szapkiw, Amanda;Tawfik, Andrew A.;Theiling, Teresa M.
• 通讯作者: Theiling, Teresa M.

From Singular Design to Differentiation: A History of Adaptive Systems

从单一设计到差异化：自适应系统的历史

• DOI: 10.1007/s11528-022-00702-3
• 发表时间: 2022
• 期刊: TechTrends
• 影响因子: 2.5
• 作者: Gatewood, Jessica;Tawfik, Andrew;Gish-Lieberman, Jaclyn J.
• 通讯作者: Gish-Lieberman, Jaclyn J.

Activity Theory as a Lens for Developing and Applying Personas and Scenarios in Learning Experience Design

活动理论作为学习体验设计中开发和应用角色和场景的镜头

• DOI: 10.51869/111/msat
• 发表时间: 2022
• 期刊: Journal of Applied Instructional Design
• 作者: Schmidt, Matthew