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We develop, advance, and promote a previously existing framework called the Qualitative-Quantitative-Qualitative workflow (Q1Q2Q3, pronounced “Q-Q-Q”) to systematically guide the content of interdisciplinary collaborations and improve the teaching of statistics and data science. The Q1Q2Q3 workflow is designed to help statisticians and data scientists develop skills and techniques for collaboration to work with domain experts across academic fields, industry sectors, and organizations. The Q1Q2Q3 workflow explicitly emphasizes the importance of the qualitative context of a project, as well as the qualitative interpretation of quantitative findings. We explain Q1Q2Q3 and provide guidance for implementing each stage of the workflow. We describe how we teach Q1Q2Q3 within a statistics and data science collaboration course and present data evaluating its effectiveness. We also describe how Q1Q2Q3 can be useful for educators teaching introductory, projects-based, and technical statistics and data science courses. We believe that the Q1Q2Q3 workflow is an easy-to-implement technique that is beneficial and necessary for statistics and data science education and practice. It can be used to weave ethics into each stage of practice so that statisticians and data scientists can successfully transform evidence into action for the benefit of society. 

ABSTRACT Reform‐oriented science classrooms encourage environments in which students engage in a collective enterprise of making sense of their science ideas together. Teachers who strive for these sorts of environments support students in collaboratively constructing and answering their own questions about phenomena and making sense of competing ideas together. However, to engage with one another productively, students must ask questions, share incomplete thoughts, and comment on each other's ideas, all of which can be seen as risky and unfamiliar behavior that may result in feelings of uncertainty or other negative classroom consequences. We conduct an explanatory case study using student and teacher interviews, teacher surveys, and classroom video collected over 2 years to investigate how one teacher used classroom norms to establish and maintain a culture in which students appeared committed to taking risks to improve their collective knowledge‐building. We found that norms were one practical tool the teacher used to encourage students to take risks and that also seemed helpful for negotiating individual and group uncertainty. Norms were also tools the teacher used to ensure that she and her students had similar expectations for classroom engagement. This study practically addresses some key challenges teachers face in enacting reform‐oriented science teaching and offers suggestions for how continued research regarding norms and uncertainty can continue to further science reform efforts. 

Due to the applied nature of statistics and data science, many educators in these fields recognize the need to teach their students how to be effective interdisciplinary collaborators. Some prior research considers different approaches to teaching interdisciplinary collaboration skills. However, missing from this literature are the connections between teaching collaboration and education theory. Thus, there is a lack of understanding about why the various pedagogical approaches may be effective. In this descriptive study, we describe an approach to teaching interdisciplinary collaboration using a Community of Practice (CoP) and highlight connections between potentially reproducible elements of this approach and education theory that explains why this approach may be effective from the perspectives of both education and collaboration theory. Our results show that students and content-area experts recognize this approach to teaching statistical and data science collaboration to be effective. By grounding our methods for teaching statistics and data science collaboration skills in education theory, we focus attention on which aspects can be replicated in other contexts, why they work well, and how they can be improved. We recommend instructors intentionally create a CoP within their courses, encourage peer mentorship, and emphasize a growth mindset. 

Abstract This article explores the challenges of enacting reform‐oriented curriculum in science classrooms. We use the concept of figured worlds to analyze a case study of an eighth‐grade science class where the teacher reported that the students were resistant to changes she was trying to make. By examining stimulated recall interviews with the teacher (including the associated classroom episodes) and post‐unit interviews with a subset of the students, we found that the students and the teacher constructed different figured worlds about the science learning in the classroom. These differences centered on the goals that students and teachers had for the class and the roles of the teacher and students in the learning environment. Specifically, we found that there was a lack of alignment around how students and the teacher viewed the purpose of student agency and collaboration and therefore they had different ideas about how they should interact with one another in the classroom. We conclude by discussing the implications of our findings for science education. We believe that the concept of figured worlds allows researchers and teachers to better understand the challenges of implementing reform‐oriented practices in science classrooms. This understanding can help teachers and professional development providers to create strategies for bridging the gap between different figured worlds and creating more collaborative and productive learning environments for all students. 

Graduate level statistics education curricula often emphasize technical instruction in theory and methodology but can fail to provide adequate practical training in applications and collaboration skills. We argue that a statistical collaboration center (“stat lab”) structured in the style of the University of Colorado Boulder’s Laboratory for Interdisciplinary Statistical Analysis (LISA) is an effective mechanism for providing graduate students with necessary training in technical, non-technical, and job-related skills. We summarize the operating structure of LISA, and then provide evidence of its positive impact on students via analyses of a survey completed by 123 collaborators who worked in LISA between 2008–15 while it was housed at Virginia Tech. Students described their work in LISA as having had a positive impact on acquiring technical (94%) and non-technical (95%) statistics skills. Five-sixths (83%) of the students reported that these skills will or have helped them advance in their careers. We call for the integration of stat labs into statistics and data science programs as part of a comprehensive and modern statistics education, and for further research on students’ experience in these labs and the impact on student outcomes. 

As practitioners and teachers of statistical consulting and collaboration, how do we assess the effectiveness of ours and our students’ engagement on projects with domain experts? We propose that assessments of the effectiveness of statistical collaborations should be based on the four areas of attitude, skills, performance, and improvement. In this brief paper, we describe several ways for conducting assessments in these four areas and conclude with a call for the statistics and data science education community to build upon these ideas. 

The questions we ask and how we ask them will make a difference in how successful we are in meetings, in collaborations and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? Great questions elicit information useful for accomplishing the tasks of a project and strengthen the statistician–domain expert relationship. Great questions have three parts: the question, the answer and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question; follow the question with behaviours and actions consistent with the prefaced words including actions such as listening, paraphrasing and summarizing; and model a collaborative relationship via the asking of a great question. We describe the methods and results of a study that shows how questions can be assessed, that statisticians can learn to ask great questions and that those who have learned this skill consider it to be valuable for their careers. We provide practical guidelines for learning how to ask great questions so that statisticians can improve their collaboration skills and thus increase their impact to help address societal challenges.