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Course-based undergraduate research experiences (CURE) provide valuable opportunities to large numbers of students relatively early in their academic careers and have the potential to attract and retain women and students from underrepresented minority groups both in the sciences and in other technical fields requiring quantitative research literacy. To evaluate the relative success of a multidisciplinary CURE, we compared background characteristics, course experiences and outcomes of men and women under-represented (URM) and non-underrepresented students. Though URM and non-URM students of both genders differed on many background characteristics, and self-reported course experiences, with few exceptions, positive course outcomes and predictors of those outcomes did not differ by URM/gender group. The Passion-Driven Statistics CURE aims to equip the future STEM workforce with the data analysis skills and reasoning needed across industries. Additional research is needed to determine whether this CURE may influence educational and career trajectories for women and URM students. 

Course-based undergraduate research experiences (CUREs) have been shown to help students gain research skills. Few studies, however, have evaluated the impact of CUREs on long-term educational and employment outcomes. This article examines postgraduation outcomes for students enrolling in introductory statistics delivered as a CURE compared with those taking a traditional mathematics-based introductory statistics course. Participating in the CURE was found to be associated with a higher likelihood of holding a job in which a primary responsibility was working with data, greater confidence in working with data, and a higher likelihood of earning more than $100K annually. This study suggests that CUREs may not only impart technical skills but also boost students’ self-efficacy in applying statistical methods and analyzing data. 

Self-efficacy is associated with a range of educational outcomes, including science and math degree attainment. Project-based statistics courses have the potential to increase students’ math self-efficacy because projects may represent a mastery experience, but students enter courses with preexisting math self-efficacy. This study explored associations between pre-course math confidence and coding confidence with post course statistical intentions and perceived achievement among students in a project-based statistics course at 28 private and public colleges and universities between fall 2018 and winter 2020 (n=801) using multilevel mixed-effects multivariate linear regression within multiply imputed data with a cross-validation approach (testing n=508 at 20 colleges/universities). We found that pre-course coding confidence was associated with, respectively, 9 points greater post-course statistical intentions and 10 points greater perceived achievement on a scale 0–100 (0.09, 95% confidence interval (0.02, 0.17), p=0.02; 0.10, 95% CI (0.01, 0.19), p=0.04), and that minoritized students have greater post-course statistical intentions than non-minoritized students. These results concur with past research showing the potential effectiveness of the project-based approach for increasing the interest of minoritized students in statistics. Pre-course interventions to increase coding confidence such as pre-college coding experiences may improve students’ post-course motivations and perceived achievement in a project-based course. Supplementary materials for this article are available online. 

The purpose of this study was to inform the dissemination of a project-based statistics curriculum by identifying institutional and instructor characteristics that predict its implementation. Data were drawn from pre- and post-workshop surveys completed by 67 instructors attending a one-and-a-half-day professional development workshop. Nearly half of the instructors who intended to implement Passion-Driven Statistics following the workshop employed the project-based curriculum by the end of the first full academic year. Teaching at a private institution and endorsing a larger number of positive adjectives regarding teachers’ likely experiences with the model predicted its actual implementation. 

This chapter describes the adaptation of the Passion-Driven Statistics curriculum to an online graduate statistics course. Employing this project-based approach, students worked in teams applying different multivariate methods to answer research questions using large, publicly available datasets. Students were able to formulate research questions based on their interests, and the class culminated in a final project for which students created a scientific poster and presented it to their colleagues. Students viewed the Passion-Driven Statistics approach favorably, and the majority felt prepared for more advanced coursework and indicated what they learned in class would assist with completion of their thesis work. 

The COVID-19 pandemic has gifted us a pivot point, an opportunity, in which we can consider ways to do things differently than we have "always" done them. Traditionally, students view statistics as an obstacle to overcome, rather than an opportunity to pursue their own interests and passions. The Passion-Driven Statistics curriculum challenges this viewpoint by exposing students to a meaningful and powerful data analysis experience during a 3-day "boot camp" or as a short project over a few weeks. This provides major student outcomes (e.g., an empirical poster presentation) with minor faculty investment (e.g., time, technology). Our model can be quickly personalized to meet the needs of you and your students, which is especially important during moments of an unexpected pivot. In addition to face-to-face, the outcomes can be met in a fully online, remote, or hybrid environment, making this model suitable for use in a variety of contexts. The "boot camp" model could serve as a way for your student lab members to gain research experience, skill-building workshop for your psychology club students, or project for a content-based course. This NSF-funded (DUE #1820766) model is a multidisciplinary, project-based curriculum that supports students in conducting original research, asking original questions, and communicating methods and results using the language of statistics. The course attracts higher rates of under-represented minority (URM) students compared to a traditional math statistics course (Dierker et al., 2015) and higher rates of female and URM students compared to an introductory programming course (Cooper & Dierker, 2017). Students reported the course more rewarding, were more likely to accomplish more than expected, found the course more useful than other courses, increased confidence in working with data, increased interest in pursuing advanced statistics courses, and received more individualized support than other courses (Dierker et al., 2018). 

Passion-Driven Statistics is a project-based, introductory curriculum implemented as a course in statistics, research methods, data science, a capstone experience, and a summer research boot camp with students from a wide variety of academic settings. Funded by the National Science Foundation, the curriculum engages students in authentic projects with large, real-world data sets. Passion-Driven Statistics students were more likely to report increased confidence in working with data and increased interest in pursuing advanced statistics course work compared to students from the traditional statistics course (Dierker et al., 2018a). This presentation draws on pre/post data from 67 instructors attending a Passion-Driven Statistics faculty development workshop. Analyses evaluate instructor characteristics, attitudes, and experiences that predict its implementation. Findings show that nearly half of the instructors who reported being likely to implement passion-driven statistics and a quarter of the overall sample employed the project-based curriculum by the end of the first full academic year following the workshop. Those showing this fast uptake were more likely to be female than those who had not yet implemented (87.5% vs. 55.3%), were more likely to hold a Ph.D. (94.1% vs. 59.2%), and were more likely to be employed by a private rather than public institution (76.5% vs. 46.0%). Those showing fast uptake were also more likely to have been previously using statistical software (i.e., SAS, JMP, R, Stata, Python, or SPSS) in their target course (70.6% vs. 40.0%) and to have greater prior experience with project-based skills and fewer post workshop concerns about their likely success. Results from these analyses will guide recommendations for 1) engaging instructors ready to implement innovation in learning and teaching and 2) supporting those instructors requiring additional time, training and skills. Instructors are the most important resource for promoting innovations and all materials are open educational resources 

To evaluate the impact of a multidisciplinary, project-based course in introductory statistics, this exploratory study examined learning experiences, feelings of confidence, and interest in future experiences with data for undergraduate students in Ghana, West Africa. Students completed a one-semester, introductory statistics course utilizing the Passion-Driven Statistics curriculum. Results showed more than half of the students put more effort into the course and found the material more challenging compared to other courses, while nearly three-quarters reported interest in one or more follow-up courses. Importantly, students also reported increased confidence in a variety of applied statistical skills. These findings demonstrate the positive impact of a multidisciplinary, project-based curriculum on undergraduate students in Ghana, West Africa and demonstrate the potential for its global portability.