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.

Artificial intelligence (AI) and machine learning (ML) systems are ubiquitous across many fields ranging from medicine (e.g., tumor detection), to natural language processing (e.g., digital home assistants, auto-translate tools), and personalization (e.g., social media recommendations). The rise of these systems has also seen a rise in the ethical challenges resulting from how some of these systems are designed and implemented. Most problematic is their propagation and amplification of problems such as racism and sexism [4, 6], among others. Further, these challenges exist within a computing discipline that is already burdened by exclusive, marginalizing cultures and practices that lead to low participation by women and Black, Indigenous, and People of Color (BIPOC) [3]. Our project, AI + Dance, attends to these dimensions of inequity within AI/ML education through developing our understanding of how we can equip learners to recognize and rectify issues of AI and ML systems within an inclusive and culturally sustaining experience. We explore this in collaboration with STEM From Dance1 (SFD), a non-profit organization that supports girls of color in creative production with dance, CS, and STEM.

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.

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.

We describe work in progress on Deep Making, a framework for the design of curricular modules that facilitate learning of transferable content-knowledge in makerspaces and FabLabs. Concern about epistemological dilution and our experience in co-design work with educators in these settings has informed this theoretical framing. We discuss the need for such a set of modules and the educational theories that guide us in the process of designing them. We present a few examples of such modules and sketch out a research design to test their efficacy.

Learning analytics is an interdisciplinary and inclusive field, a fact which makes the establishment of methodological norms both challenging and important. This community-building workshop intends to convene methodology-focused researchers to discuss new and established approaches, comment on the state of current practice, author pedagogical manuscripts, and co-develop guidelines to help move the field forward with quality and rigor.

This paper explores a longitudinal approach to combining engagement, performance and social connectivity data from a MOOC using the framework of exponential random graph models (ERGMs). The idea is to model the social network in the discussion forum in a given week not only using performance (assignment scores) and overall engagement (lecture and discussion views) covariates within that week, but also on the same person-level covariates from adjacent previous and subsequent weeks. We find that over all eight weekly sessions, the social networks constructed from the forum interactions are relatively sparse and lack the tendency for preferential attachment. By analyzing data from the second week, we also find that individuals with higher performance scores from current, previous, and future weeks tend to be more connected in the social network. Engagement with lectures had significant but sometimes puzzling effects on social connectivity. However, the relationships between social connectivity, performance, and engagement weakened over time, and results were not stable across weeks.

Scoring of student item response data from online courses and especially massively open online courses (MOOCs) is complicated by two challenges, potentially large amounts of missing data and allowances for multiple attempts to answer. Approaches to ability estimation with respect to both of these issues are considered using data from a large-enrollment electrical engineering MOOC. The allowance of unlimited multiple attempts sets up a range of observed score and latent-variable approaches to scoring the constructed response homework. With respect to missing data, two classical approaches are discussed, treating omitted items as incorrect or missing at random (MAR). These treatments turn out to have slightly different interpretations depending on the scoring model. In all, twelve different homework scores are proposed based on combinations of scoring model and missing data handling. The scores are computed and correlations between each score and the final exam score are compared, with attention to different populations of course participants.