Creating Pathways for Data Proficiency in Undergraduate Students

为本科生的数据熟练程度创造途径

• 批准号: 1712296
• 负责人: Anna Bargagliotti
• 金额: $ 26.59万
• 依托单位: Loyola Marymount University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712296&HistoricalAwards=false
• 关键词: Creating; Pathways; Data; Proficiency; Undergraduate

The need for a workforce proficient in statistics, and more recently, data science, continues to increase. In higher education nationally, statistics courses are frequently taught in a variety of disciplines, such as mathematics, biology, economics, engineering, political science, psychology, business, and education. These courses tend to have discipline-specific motivations and content emphases, as well as discipline-specific examples and exercises from which students are expected to learn general concepts in statistics. Students often are not well served by taking these courses since many of them do not fulfill the prerequisite requirements for subsequent statistics or data science courses offered by other departments. Furthermore, these courses are not necessarily aligned with general recommendations from professional organizations and standards of statistical practice. The Curriculum Guidelines for Undergraduate Programs in Statistics, from the American Statistical Association (ASA), recommends that courses should emphasize working with real data, working with technology, and communicating ideas. A main goal of this proposal is to research how to create pathways in which students can become data proficient. Bringing together representatives from mathematics, biology, economics, engineering, political science, psychology, business, and education at LMU, this project will work toward building cohesion among the introductory statistics courses with an emphasis on those three recommendations from the ASA report.Focusing on the needed institutional change to create pathways, the project will examine the similarities and differences of how the three themes are manifested in statistics courses across disciplines, and study faculty and student engagement with the three themes in their teaching and learning. Data sources will include faculty surveys, classroom observations, collections of classroom materials and student work, and multi-disciplinary faculty discourse discussing similarities and differences of statistics across disciplines. By cross-validating the data sources, the project will address the research questions: (1) What are common learning objectives, outcomes, and experiences in introductory and advanced statistics courses across disciplines and how do these align with ASA guidelines? and (2) To what extent does faculty teaching and student work demonstrate engagement with the three themes of real data, technology, and communication across disciplines?

对精通统计以及最近的数据科学的劳动力的需求不断增加。在全国的高等教育中，统计学课程经常在各种学科中教授，如数学、生物学、经济学、工程学、政治学、心理学、商学和教育学。这些课程往往有特定学科的动机和内容重点，以及特定学科的例子和练习，学生希望从中学习统计学的一般概念。学生往往不能很好地通过这些课程，因为他们中的许多人不符合其他部门提供的后续统计或数据科学课程的先决条件。此外，这些课程不一定符合专业组织的一般建议和统计实践标准。美国统计协会（ASA）的《统计学本科课程指南》建议，课程应强调使用真实数据、使用技术和交流思想。本提案的一个主要目标是研究如何创造学生能够精通数据的途径。该项目汇集了LMU来自数学、生物学、经济学、工程学、政治学、心理学、商学和教育学的代表，将致力于建立统计学入门课程之间的凝聚力，重点是ASA报告中的这三项建议。该项目将着眼于创造途径所需的制度变革，研究这三个主题在各学科统计学课程中表现的异同，并研究教师和学生在教学和学习中对这三个主题的参与情况。数据来源将包括教师调查、课堂观察、课堂材料和学生作业的收集，以及讨论跨学科统计的异同的多学科教师话语。通过交叉验证数据源，该项目将解决以下研究问题：(1)跨学科的入门和高级统计学课程的共同学习目标、成果和经验是什么，这些如何与ASA指南保持一致？(2)教师教学和学生工作在多大程度上展示了对真实数据、技术和跨学科交流这三个主题的参与？

项目成果

Undergraduate Learning Outcomes for Achieving Data Acumen

实现数据敏锐度的本科生学习成果

• DOI: 10.1080/10691898.2020.1776653
• 发表时间: 2020
• 期刊: Journal of Statistics Education
• 影响因子: 2.2
• 作者: Bargagliotti, Anna;Binder, Wendy;Blakesley, Lance;Eusufzai, Zaki;Fitzpatrick, Ben;Ford, Maire;Huchting, Karen;Larson, Suzanne;Miric, Natasha;Rovetti, Robert
• 通讯作者: Rovetti, Robert