Creating Pathways for Data Proficiency in Undergraduate Students

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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?