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

该项目旨在通过改善统计和数据科学的本科生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职员工对统计和数据科学的态度来做到这一点。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的见解的能力。使用这些工具来使用和操纵数据集，对于今天的STEM工作人员以及精通数据的公民来说，都是一项关键技能。然而，让教员成功地教授和学生成功地学习这些技能仍然是具有挑战性的。拟议的项目希望通过加深对学生和教师对统计和数据科学的态度的理解，在更好地教授和学习统计和数据科学方面取得进展。这种理解反过来又有助于开发有效的方法来教授和学习统计和数据科学，并确定什么对培养有技能和积极进取的统计学家和数据科学家最有效。目前，几乎没有评估对统计和数据科学的态度的工具，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，并将其公之于众。将建立一个可持续的基础设施，以便利持续的数据收集和成果传播。不同的学生群体将被纳入所有阶段，以确定对特定学习者群体特别有效的挑战、机会和教学实践。NSF改善本科生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，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.