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

数据驱动的多学科课程，通过基于项目的学习提供进入数据分析经济的机会

基本信息

批准号：
1820766
负责人：
Lisa Dierker
金额：
$ 282.5万
依托单位：
Wesleyan University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820766&HistoricalAwards=false
关键词：
Data Driven Multidisciplinary Curriculum Providing

项目摘要

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.

现在，大多数收入最高、需求旺盛的工作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。该项目旨在为未来的STEM劳动力提供推动跨行业创新所需的数据分析技能。为此，本项目将传播基于项目的数据分析课程，使学生能够在自己的研究项目中使用领先的分析平台（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调查的力量和兴奋，无论他们的准备或最初的兴趣如何。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。根据项目估计，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。通过使数据科学教育更容易获得，该项目旨在增加女性和其他代表性不足的学生的招聘和保留，使其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，他们拥有跨行业、学科和受众所需的数据分析技能，从而有助于国家在全球经济中的竞争力。该计划旨在利用新的实施地点的现有基础设施，并整合一套协调的循证实践，以支持学生和教师学习和参与具有大型数据集的研究项目。项目的主要特征包括：1)基于项目的学习与学习者的兴趣和内在动机相联系；2)多学科探究的机会；3)在现实世界背景下的大数据集分析；4)编程作为数据驱动推理和沟通的窗口；5)密集的，以学生为中心的一对一支持，利用基于证据的策略来促进弱势青年的成功。该项目将采用事前/事后调查，准实验设计，采用最先进的因果推理技术，以及机构数据，回答五个研究问题：课程是否对学生产生积极的影响？课程是否增加了女性、代表性不足的学生和有学习障碍的学生的数据分析技能？教育工作者培训和专业发展的模式是否有效地培养知识和信心？在多大程度上，参与的教育工作者在他们的课程和课堂中应用和维持基于项目的模式？在机构层面，什么样的形式和财政支持模式能为数据驱动的课程提供最大的可持续性？通过开展包括教育工作者培训、项目可持续性和学生成绩等领域的评估研究，该项目将为教学和学习数据分析提供新的知识。该奖项反映了美国国家科学基金会的法定使命，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

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Evaluating Impact: A Comparison of Learning Experiences and Outcomes of Students Completing A Traditional Versus Multidisciplinary, Project-Based Introductory Statistics Course

评估影响：完成传统与多学科、基于项目的入门统计课程的学生的学习经历和成果的比较

DOI：
10.33094/6.2017.2018.21.16.28
发表时间：
2018
期刊：
Training and Learning
影响因子：
0
作者：
Lisa, Dierker;Kristin, Flaming;L, Cooper;Karen, Singer-Freeman;Kaori, Germano;Jennifer, Rose
通讯作者：
Jennifer, Rose

Passion-Driven Statistics: A course-based undergraduate research experience (CURE)

激情驱动的统计学：基于课程的本科生研究经验（CURE）

DOI：
10.54870/1551-3440.1575
发表时间：
2022
期刊：
The Mathematics Enthusiast
影响因子：
0
作者：
Spence, Naomi J.;Anderson, Rachel;Corrow, Sherryse;Dumais, Susan A.;Dierker, Lisa
通讯作者：
Dierker, Lisa

Adapting the Passion-Driven Statistics Curriculum for an Online Graduate Multivariate Statistics Course

为在线研究生多元统计课程调整激情驱动的统计课程