Developing Validated Instruments to Measure Student/Faculty Attitudes in Undergraduate Statistics and Data Science Education

开发经过验证的工具来衡量本科统计和数据科学教育中学生/教师的态度

• 批准号: 2013392
• 负责人: Alana Unfried
• 金额: $ 60万
• 依托单位: University Corporation at Monterey Bay
• 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: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013392&HistoricalAwards=false
• 关键词: Developing; Validated; Instruments; Measure; Student

This project aims to serve the national interest by improving undergraduate teaching and learning of statistics and data science. It will do so by developing better ways to measure and improve student and faculty attitudes toward statistics and data science. Statistics is the science of collection, analysis, interpretation, and presentation of numerical data. Data science is ability to extract actionable insights from data of all types. Using these tools to use and manipulate data sets is a critical skill for today's STEM workforce, as well as for a data-savvy citizenry. However, engaging faculty to successfully teach and students to successfully learn these skills continues to be challenging. The proposed project expects to make progress toward better teaching and learning of statistics and data science by developing a deeper understanding about student and instructor attitudes toward these topics. This understanding, in turn, can help in developing effective ways to teach and learn statistics and data science and to identify what works best for educating skilled and motivated statisticians and data scientists.Positive attitudes have been shown to be essential to student learning of all types. Currently, few instruments exist for assessing attitudes toward statistics and data science, and these have critical flaws. This project will collect data from a nationally representative sample of undergraduate students and their instructors to develop and statistically validate new instruments for these purposes. The final data set will be analyzed for trends that may indicate best practices in statistics and data science education and be made publicly available. A sustainable infrastructure will be created to facilitate ongoing data collection and dissemination of results. A diversity of student populations will be included in all phases to identify challenges, opportunities, and pedagogical practices that are particularly effective for specific groups of learners. The NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR) Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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统计和数据科学的本科教学和学习来服务于国家利益。 它将通过开发更好的方法来衡量和改善学生和教师对统计和数据科学的态度。 统计学是收集、分析、解释和呈现数字数据的科学。 数据科学是从所有类型的数据中提取可操作见解的能力。使用这些工具来使用和操作数据集是当今STEM劳动力以及精通数据的公民的关键技能。 然而，吸引教师成功地教和学生成功地学习这些技能仍然是具有挑战性的。 拟议的项目预计将通过更深入地了解学生和教师对这些主题的态度，在更好地教授和学习统计和数据科学方面取得进展。 反过来，这种理解可以帮助制定有效的方法来教授和学习统计和数据科学，并确定什么最适合培养有技能和有动力的统计人员和数据科学家。目前，用于评估对统计和数据科学的态度的工具很少，这些工具存在严重缺陷。该项目将从全国有代表性的本科生及其教师样本中收集数据，以开发和统计验证用于这些目的的新工具。将对最终数据集进行分析，以确定可能表明统计和数据科学教育最佳做法的趋势，并将其公布于众。将建立一个可持续的基础设施，以促进持续的数据收集和成果传播。学生群体的多样性将包括在所有阶段，以确定挑战，机遇和教学实践，特别是对特定群体的学习者有效。NSF改进本科STEM教育计划：教育和人力资源（IUSE：EHR）计划支持研究和开发项目，以提高所有学生STEM教育的有效性。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

CHALLENGES ASSOCIATED WITH MEASURING ATTITUDES USING THE SATS FAMILY OF INSTRUMENTS

• DOI: 10.52041/serj.v21i1.88
• 发表时间: 2022-03
• 期刊: STATISTICS EDUCATION RESEARCH JOURNAL
• 作者: Douglas Whitaker;A. Unfried;Marjorie E. Bond

A New Survey of Student Attitudes Toward Statistics: The S-SOMAS

学生对统计学态度的新调查：S-SOMAS

• DOI: 10.52041/iase.icots11.t14d1
• 发表时间: 2022
• 期刊: Argentina.
• 作者: Whitaker, Douglas;Unfried, Alana;Batakci, Leyla;Bolon, Wendine;Bond, Marjorie;Kerby-Helm, April;Posner, Michael
• 通讯作者: Posner, Michael

S-SOMADS: A New Survey to Measure Student Attitudes Toward Data Science

S-SOMADS：一项衡量学生对数据科学态度的新调查

• DOI: 10.52041/iase.icots11.t8a2
• 发表时间: 2022
• 期刊: Argentina.
• 作者: Kerby-Helm, April;Posner, Michael;Unfried, Alana;Whitaker, Douglas;Bond, Marjorie;Batakci, Leyla;Bolon, Wendine
• 通讯作者: Bolon, Wendine

A Model for the Classroom Environment

课堂环境模型

• DOI: 10.52041/iase.icots11.t8a3
• 发表时间: 2022
• 期刊: Argentina.
• 作者: Bond, Marjorie;Batakci, Leyla;Whitaker, Douglas;Bolon, Wendine;Kerby-Helm, April;Unfried, Alana;Posner, Michael
• 通讯作者: Posner, Michael

The Big Picture: A Family of Instruments for Understanding University-Level Statistics and Data Science Attitudes

大局观：了解大学级统计和数据科学态度的一系列工具

• DOI: 10.52041/iase.icots11.t8a1
• 发表时间: 2022
• 期刊: Argentina.
• 作者: Unfried, Alana;Whitaker, Douglas;Batackci, Leyla;Bolon, Wendine;Bond, Marjorie;Kerby-Helm, April;Posner, Michael
• 通讯作者: Posner, Michael