Youths’ critical evaluation of AI is key in supporting their informed interactions with AI systems. We report on a five-day AI literacy camp with teens (N = 16), where they used a custom text-to-image interface to systematically evaluate GenAI. We examined: (1) how youth defined “goodness” in AI behavior, (2) applied (or did not) their ideas of goodness to AI evaluation, and (3) challenges that emerged when supporting systematic evaluation. Youth defined goodness as prompt adherence, realism, and representation. However, in their evaluations, they often conflated personal preference (e.g., personally enjoyable content) with output quality and noticed surface-level patterns (sometimes without deeper causal interpretation). Structured comparisons and peer sensemaking helped youth produce more specific claims. We conclude by reflecting on challenges and opportunities and suggest design implications.

This workshop will bring together researchers and educators to imagine a future of low-cost, widely-available digital making for children, both within the STEAM classroom and beyond. In particular, we are interested in expanding the reach of digital making with programmable microcontrollers (such as Arduino, the BBC micro:bit, etc.) to underrepresented children in the STEAM fields, which includes historically excluded or marginalized children as well as those lacking access to computers and/or the Internet. Participants will report on their experience helping children learn about digital technology while creating wearables, robotics, environmental sensors and more. Participants who submit a position paper or work-in-progress report will have an opportunity to present their work and ideas. From these presentations, we will select emerging themes to discuss.

Much of early literacy education happens at home with caretakers reading books to young children. Prior research demonstrates how having dialogue with children during co-reading can develop critical reading readiness skills, but most adult readers are unsure if and how to lead effective conversations. We present ContextQ, a tablet-based reading application to unobtrusively present auto-generated dialogic questions to caretakers to support this dialogic reading practice. An ablation study demonstrates how our method of encoding educator expertise into the question generation pipeline can produce high-quality output; and through a user study with 12 parent-child dyads (child age: 4–6), we demonstrate that this system can serve as a guide for parents in leading contextually meaningful dialogue, leading to significantly more conversational turns from both the parent and the child and deeper conversations with connections to the child’s everyday life.

Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo modeling experiences limit valuable opportunities for encountering alternative ideas and approaches that can arise when learners work together; consequently, it often precludes encountering critical issues in ML around data representation and diversity that can surface when different perspectives are manifested in a group-constructed data set. To address this issue, we created Co-ML – a tablet-based app for learners to collaboratively build ML image classifiers through an end-to-end, iterative model-building process. In this paper, we illustrate the feasibility and potential richness of collaborative modeling by presenting an in-depth case study of a family (two children 11 and 14-years-old working with their parents) using Co-ML in a facilitated introductory ML activity at home. We share the Co-ML system design and contribute a discussion of how using Co-ML in a collaborative activity enabled beginners to collectively engage with dataset design considerations underrepresented in prior work such as data diversity, class imbalance, and data quality. We discuss how a distributed collaborative process, in which individuals can take on different model-building responsibilities, provides a rich context for children and adults to learn ML dataset design.

Typical educational robotics approaches rely on imperative programming for robot navigation. However, with the increasing presence of AI in everyday life, these approaches miss an opportunity to introduce machine learning (ML) techniques grounded in an authentic and engaging learning context. Furthermore, the needs for costly specialized equipment and ample physical space are barriers that limit access to robotics experiences for all learners. We propose ARtonomous, a relatively low-cost, virtual alternative to physical, programming-only robotics kits. With ARtonomous, students employ reinforcement learning (RL) alongside code to train and customize virtual autonomous robotic vehicles. Through a study evaluating ARtonomous, we found that middle-school students developed an understanding of RL, reported high levels of engagement, and demonstrated curiosity for learning more about ML. This research demonstrates the feasibility of an approach like ARtonomous for 1) eliminating barriers to robotics education and 2) promoting student learning and interest in RL and ML.

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.

Machine learning (ML) technologies are ubiquitous and increasingly influential in daily life. They are powerful tools people can use to build creative, personalized systems in a wide variety of contexts. We believe ML has vast potential for young people to use to make creative projects, especially when used in conjunction with programming. This potential is understudied. We know little about what projects youth might create, or what computational practices they could engage in while building them. We combined a beginner-level ML modeling toolkit with a beginning programming tool and then investigated how young people created and remixed projects to incorporate custom ML-based gestural inputs. We found that (1) participants were able to build and integrate ML models of their own gestures into programming projects; (2) the design of their gestures ranged from coherent to disjoint with respect to the narratives, characters, and actions of their interactive worlds; and (3) they tested their projects by assessing the programmed vs. modeled aspects of them as distinct units. We conclude with a discussion of how we might support youth in combining code and ML modeling going forward.

The 32 chapters of the 2019 Cambridge Handbook of Computing Education Research synthesize the existing research in computing education and propose new directions for future research. An author from each chapter will summarize their chapter with auto-advancing slides. Attendees will be introduced to the breadth of content in the new handbook and can identify chapters of interest. This fits uniquely as a special session, and will likely be informative, inspiring, and overwhelming.

Machine Learning-based (ML) technologies impact many facets of our lives. Given ML's ubiquity, and the ways it offers creative computational possibilities distinct from programming, we believe it could be a powerful tool for youth to leverage in making, creativity, and play. We investigate how youth with no programming experience can incorporate ML classifiers into athletic practice by building models of their own physical activity. In this paper, we describe a design experiment exploring how to introduce youth to making ML models within the context of their athletic interests. We present AlpacaML, an iOS application that connects to wearable sensors and allows young people to model physical movement using an ML classifier, and detail its use in a three-hour workshop with middle- and high-school athletes. We found the youth were able to collect data, build models, test and evaluate models, and quickly iterate on this process. We finish with a discussion of why this is a promising direction for the incorporation of Machine Learning into novice youth making, exploration, and play.

Ibram X. Kendi, in his book Stamped from the Beginning, lays out the argument that structures (political, economic) drive inequitable systems, and it is our enculturation in those systems that lead to ignorance and bigotry. Our goal, in this BOF, is to discuss the dominant social, political, and curricular structures of the discipline of computing, and how we might strengthen or challenge those structures to increase access, equity, and justice within computing. To do so, we must confront the manifold ways in which the existing structures and practices of computing are exclusionary, from curricula to epistemology and the expressions of identity that typically represent what it means to be (and be recognized as) a computer scientist.

editorial Free Access Share on Introduction to the Special Section: Launching an Agenda for Research on Learning Machine Learning Authors: R. Benjamin Shapiro Department of Computer Science, University of Colorado Boulder Department of Computer Science, University of Colorado BoulderView Profile , Rebecca Fiebrink Department of Computing, Goldsmiths University of London Department of Computing, Goldsmiths University of LondonView Profile Authors Info & Claims ACM Transactions on Computing EducationVolume 19Issue 4December 2019 Article No.: 30pp 1–6https://doi.org/10.1145/3354136Published:10 October 2019Publication History 2citation570DownloadsMetricsTotal Citations2Total Downloads570Last 12 Months133Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Paper-based fabrication techniques offer powerful opportunities to prototype new technological interfaces. Typically, paper-based interfaces are either static mockups or require integration with sensors to provide real-time interactivity. The latter can be challenging and expensive, requiring knowledge of electronics, programming, and sensing. But what if computer vision could be combined with prototyping domain-aware programming tools to support the rapid construction of interactive, paper-based tangible interfaces? We designed a toolkit called ARcadia that allows for rapid, low-cost prototyping of TUIs that only requires access to a webcam, a web browser, and paper. ARcadia brings paper prototypes to life through the use of marker based augmented reality (AR). Users create mappings between real-world tangible objects and different UI elements. After a crafting and programming phase, all subsequent interactions take place with the tangible objects. We evaluated ARcadia in a workshop with 120 teenage girls and found that tangible AR technologies can empower novice technology designers to rapidly construct and iterate on their ideas.

Online communities dedicated to the creation of fanworks (e.g., fiction or art inspired by media such as books or television shows) often serve as communities of practice for learning communication, artistic, and technical skills. In studying one successful fan fiction archive that was designed and built entirely by (predominantly women) fans, we observed processes of legitimate peripheral participation (LPP) in which some of these fans began in peripheral roles and came to be more involved in the technical aspects of the archive over time. In addition to outlining positive outcomes, we discuss the challenges of supporting learning within this CoP, particularly with respect to the burden on experts. We discuss potential implications and solutions for the problem of expert scarcity in CoPs, and propose that LPP within fan communities can be leveraged for broadening participation in computing among women.

Research on students' learning in computing typically investigates how to enable individuals to develop concepts and skills, yet many forms of computing education, from peer instruction to robotics competitions, involve group work in which understanding may not be entirely locatable within individuals' minds. We need theories and methods that allow us to understand learning in cognitive systems: culturally and historically situated groups of students, teachers, and tools. Accordingly, we draw on Hutchins' Distributed Cognition [16] theory to present a qualitative case study analysis of interaction and learning within a small group of middle school students programming computer music. Our analysis shows how a system of students, teachers, and tools, working in a music classroom, is able to accomplish conceptually demanding computer music programming. We show how the system does this by 1) collectively drawing on individuals' knowledge, 2) using the physical and virtual affordances of different tools to organize work, externalize knowledge, and create new demands for problem solving, and 3) reconfiguring relationships between individuals and tools over time as the focus of problem solving changes. We discuss the implications of this perspective for research on teaching, learning and assessment in computing.

CS teacher development has become a major effort for the SIGCSE community in part due to NSF's CS10K efforts and expanding CSTA involvement. However there are few examples of university courses explicitly designed to train CS teachers. We do not yet have clarity on the topics CS education methods courses should cover and how best to prepare teachers to teach learners new to computational problem solving and programming. As an interdisciplinary field of study, CS education must necessarily draw on domain knowledge in CS, research in computing education, as well as research in education and the learning sciences about how students learn, both generally and in computing. At the same time, a methods course must provide prospective teachers with practical, hands-on experiences wherein they integrate research-based best practices with age-appropriate content for their target student population.

Interactive technologies have become an important part of teaching and learning. However, the data that these systems generate is increasingly unstructured, complex, and therefore difficult of which to make sense of. Current computationally driven methods (e.g., latent semantic analysis or learning based image classifiers) for classifying student contributions don't include the ability to function on multimodal artifacts (e.g., sketches, videos, or annotated images) that new technologies enable. We have developed and implemented a classifcation algorithm based on learners' interactions with the artifacts they create. This new form of semi-automated concept classification, coined Collaborative Spatial Classification, leverages the spatial arrangement of artifacts to provide a visualization that generates summary level data about about idea distribution. This approach has two benefits. First, students learn to identify and articulate patterns and connections among classmates ideas. Second, the teacher receives a high-level view of the distribution of ideas, enabling them to decide how to shift their instructional practices in real-time.

In this paper we present an initial study of how metaphors are used by university-level Computer Science instructors. The goal of this research is to gain a better understanding of the role that metaphors play in Computer Science education, to catalog the kinds of metaphors that are used, and to assess their effectiveness in supporting learning. We interviewed 10 educators in Computer Science about the metaphors they have used in the classroom, with a focus on introductory "CS1" programming courses. We analyze these interviews with an existing theory of metaphors, which provides a framework for describing their structure and features. The theory predicts that most metaphors have limitations, and eventually fall apart. Therefore, we also asked educators to assess how far they could push their metaphors with and to describe what happens at the breaking point. Our preliminary findings provide a foundation to inform and guide more in-depth analyses in the future.