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

本项目旨在通过提高本科统计与数据科学的教与学，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学和数据科学的态度来实现这一目标。统计学是一门收集、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员工以及精通数据的公民的关键技能。然而，让教师成功地教授和学生成功地学习这些技能仍然是一项挑战。拟议的项目希望通过加深对学生和教师对这些主题的态度的理解，在统计学和数据科学的教学方面取得进展。反过来，这种理解可以帮助开发有效的方法来教授和学习统计和数据科学，并确定最适合培养熟练和积极的统计学家和数据科学家的方法。积极的态度已被证明对所有类型的学生学习都是必不可少的。目前，很少有工具用于评估对统计和数据科学的态度，而且这些工具存在严重缺陷。该项目将从具有全国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。将对最终的数据集进行分析，找出可能表明统计和数据科学教育最佳做法的趋势，并向公众提供。将建立一个可持续的基础设施，以促进不断收集数据和传播结果。所有阶段都将包括学生群体的多样性，以确定挑战、机遇和对特定学习者群体特别有效的教学实践。美国国家科学基金会改善本科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。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。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。

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.