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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. 

Wearable electronics expand the ways learners can create with computing as they gain proficiency with programming and electronics. Dance is one domain where wearables can support creative, embodied practices in computing education. However, wearable electronics need to be small, durable, and easily integrated into clothing to meet the constraints of dance contexts. These features are challenging to achieve, especially when working with novices. We present DanceBits, a wearable prototyping kit for dance that was co-developed with a justice-oriented, computing and dance education organization. DanceBits’ plug-and-play system uses small PCBs with solderless connectors to support dancers in rapidly designing, building, and performing with electronic costumes. Our user studies exploring the system with dance instructors and youth participants show that DanceBits enabled fast development of wearables, offered users a breadth of expressivity through computational and choreographic choices, and empowered dancers to see wearables as a tool for developing their movement practices. 

Performing arts computing environments have received little attention in the educational sphere; yet, they offer opportunities for learners to validate their efforts, ideas, and skills through showcasing their work in a public-facing performance. In this work, we explore an out-of-school dance and computing educational program run by the organization, STEM From Dance. The organizational mission is to create an equitable learning experience for young women of color to engage with computing while exposing them to STEM careers. Through an analysis of eleven interviews with youth participants, instructors, and the executive director, we examine how the social, cultural, and political dimensions of the learning environment facilitate identity work in computing and dance. Our findings point to three primary activities used by the organization to promote equity: (1) providing psychological safety through a supportive community environment, (2) meaningfully engaging with learners’ social and cultural context through creative work with constructionist artifacts, and (3) actively promoting identity work as women of color in computing and STEM through both artifact work and community events. Applying the constructs of identity and psychological safety we explore the tensions and synergies of designing for equity in this performing arts and computing learning environment. We demonstrate how the seemingly contradictory elements of a high-stakes performance within a novice learning environment provides unique opportunities for supporting young women of color in computing, making them non-negotiable in the organization’s efforts to promote equity and inclusion. Our work illustrates how attending closely to the sociocultural dimensions in a constructionist learning environment provides lenses for navigating equity, identity work, and support for inclusive computing. 

Our research collaborative has been exploring movement computing educational technology experiences. That is, we have been building tools that simultaneously support both movement and computing learning objectives at entry-level. We will demo two products in development. danceON is a domain-specific language and a web app that allows users to create interactive graphics overlaid on video from pre-recorded or live (webcam) sources. soft- WEAR is a solderless and breadboardless ecosystem using sensors, LEDs, and the Adafruit Trinket M0. It is designed to support a workflow from ideation, prototyping, and iteration to a durable, wearable final project embedded into clothing or accessories. 

This paper takes a theoretical approach to movement computing education for young learners, with a focus on middle grades (grades 6-8, ages 11-14). This age group is targeted as a lower bound because, while some elements of computational thinking may be available to still younger learners, there are abstractions involved in movement computation that pre-require a certain amount of formal operation, in the Piagetian sense. We outline a parallel foundation of key ideas in movement (specifically dance) and key ideas in computing (specifically data representations) at this age-appropriate level. We describe how these foundations might be laid down together early on so that they can later be integrated via the introduction of sensing and feedback technology. Concepts in movement and choreography are studied using words and bodies, as in traditional dance education, and later using computer simulations and motion capture. Data concepts are introduced first by appeal to general questions and later by specification to the movement of individual and collective joints and bodies. 

Dance provides unique opportunities for embodied interdisciplinary learning experiences that can be personally and culturally relevant. danceON is a system that supports learners to leverage their body movement as they engage in artistic practices across data science, computing, and dance. The technology includes a Domain Specific Language (DSL) with declarative syntax and reactive behavior, a media player with pose detection and classification, and a web-based IDE. danceON provides a low-floor allowing users to bind virtual shapes to body positions in under three lines of code, while also enabling complex, dynamic animations that users can design working with conditionals and past position data. We developed danceON to support distance learning and deployed it in two consecutive cohorts of a remote, two-week summer camp for young women of color. We present our findings from an analysis of the experience and the resulting computational performances. The work identifies implications for how design can support learners’ expression across culturally relevant themes and examines challenges from the lens of usability of the computing language and technology. 

A key promise of adaptive collaborative learning support is the ability to improve learning outcomes by providing individual students with the help they need to collaborate more effectively. These systems have focused on a single platform. However, recent technology-supported collaborative learning platforms allow students to collaborate in different contexts: computer-supported classroom environments, network based online learning environments, or virtual learning environments with pedagogical agents. Our goal is to better understand how students participate in collaborative behaviors across platforms, focusing on a specific type of collaboration - help-giving. We conducted a classroom study (N = 20) to understand how students engage in help-giving across two platforms: an interactive digital learning environment and an online Q&A community. The results indicate that help-giving behavior across the two platforms is mostly influenced by the context rather than by individual differences. We discuss the implications of the results and suggest design recommendations for developing an adaptive collaborative learning support system that promotes learning and transfer.