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Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation. 

Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students’ learning. Supplementary materials for this article are available online. 

In 2021, National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) directorate implemented a Broadening Participation in Computing (BPC) plan requirement for all medium and larger research proposals in Core, CPS, and SaTC. This panel comprises faculty and administrators from US computing departments who have participated in the writing of Departmental or Project BPC plans, two in response to NSF’s encouragement and one prior. Panelists represent a range of institutions as well as departmental awareness of BPC prior to writing their plans. Regardless of where they or their departments lie in the spectrum of knowing about and implementing BPC activities, and regardless of the current demographic makeup of the students in their major, they all encountered challenges as they wrote their plans. They all also experienced successes, not the least of which is that they succeeded in getting a plan written in accordance with the current guidelines. With the support of a moderator, the three panelists will share their experiences developing BPC plans with the audience, offering lessons learned and tips for overcoming common challenges. Audience members will also receive helpful links and handouts to facilitate the writing of their own departmental or project plans 

It is widely accepted that active learning and group work generally enhance learning in the statistics classroom, but how should those groups be formed? This study aims to better understand the characteristics of a productive team in the undergraduate introductory statistics course. Specifically, we explore the relationship between the attitudes of a student’s teammates and that student’s academic performance in both individual and group settings. We find moderate evidence that positive teammate attitudes towards statistics are associated with greater improvement from a student’s individual to the team exam score. If we can better understand what combination of student characteristics results in productive teams, instructors can be intentional with how they form groups in the classroom, realizing the full efficacy of active learning. 

In July 2021, Computer Science (CS) standards were officially added as a subject area within the K-12 Montana content standards. However, due to a lack of professional development and pre-service preparation in CS, schools and teachers in Montana are underprepared to implement these standards. Montana is also a unique state, since American Indian education is mandated by the state constitution in what is known as the Indian Education for All Act. We are developing elementary and middle school units and teacher training materials that simultaneously address CS, Indian Education, and other Montana content standards. In this paper, we present a unit for fourth through sixth grades using a participatory design approach. Through physical computing, students create a visual narrative of their own stories inspired by ledger art, an American Indian art medium for recording lived experiences. We discuss the affordances and challenges of an integrated approach to CS teaching and learning in elementary and middle schools in Montana.