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This study builds on posthumanist and new materialist orientations to examine the role of material properties and the gendered identity texts of educational tools as active agents in STEM learning. Over 200 youth, ages 5–15, were randomly assigned to 90-minute introductions to one of five commercial circuitry toolkits. Youth took a pre- and post-assessment; we analyzed results using quantitative tests of significance. We used an established sorting task to gather youth perspectives of the tools as identity texts through design markers of gendered identities within the toolkits. We examined the relationship between learning outcomes and the gendered design components of the toolkits. Toolkits that privilege feminine or artistic elements significantly impacted learning more than traditional toolkits used in schools, which showed little to no significant learning gains. We relate this to the inextricability of materiality and the gendered identities of these tools and materials. This study shows how arts-based or feminine-coded tools can be more effective for teaching and learning, serving as a counter to common resistance to adopting such tools and materials for STEM learning. We outline design implications for toolkits and educational experiences to disrupt stagnant social, cultural, and historical norms in STEM education. We would like to thank the participants for their generous participation in this work, as well as our partner schools and organizations who helped make this possible. We would also like to thank members of the Creativity Labs—past and present—who helped to serve as thought partners and sounding boards at various points in the process. 

An advantage of Large Language Models (LLMs) is their contextualization capability – providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback. We present a design and evaluation of a proof-of-concept LLM application to offer students dynamic and contextualized feedback. Specifically, we augment an Online Programming Exercise bot for a college-level Cloud Computing course with ChatGPT, which offers students contextualized reflection triggers during a collaborative query optimization task in database design. We demonstrate that LLMs can be used to generate highly situated reflection triggers that incorporate details of the collaborative discussion happening in context. We discuss in depth the exploration of the design space of the triggers and their correspondence with the learning objectives as well as the impact on student learning in a pilot study with 34 students. 

This article challenges an over‐reliance on language as the primary means to communicate knowledge by adopting a languagelessness approach to maker pedagogies and maker literacies. Having conducted makerspace and design‐based research for some time, we separately and together noticed a productive relationship between wordless relational makerspace and making moments focused on craft, tools, technologies, and materials, and ways that an absence of verbal and written communication opens possibilities within learning environments. After meetings and discussions, we co‐wrote the article to examine ways that language‐light, even language‐free pedagogical spaces allow for a different quality of design work that motivates and fosters innovation. There are three international research projects that serve as research vignettes to investigate the efficacy of languagelessness. The theory foregrounded to anchor and interpret the three vignettes draws from maker literacies research and sociomaterial orientations to knowledge development. 

We draw on constructionism to design a group rope weaving activity as an illustration of concepts in matrix algebra. The study, conducted in an undergraduate Informatics class, involved a rope-weaving activity to assess the concept of cloth separability and the matrix representation of weaving patterns. Results showed significant learning gains, with distinct approaches observed: one group emphasizing physical manipulation, the other relying on mathematical principles. This study underscores the value of tangible engagement in understanding abstract concepts. 

We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora. 

Weaving is a fabrication process that is grounded in mathematics and engineering: from the binary, matrix-like nature of the pattern drafts weavers have used for centuries, to the punch card programming of the first Jacquard looms. This intersection of disciplines provides an opportunity to ground abstract mathematical concepts in a concrete and embodied art, viewing this textile art through the lens of engineering. Currently, available looms are not optimized to take advantage of this opportunity to increase mathematics learning by providing hands-on interdisciplinary learning in collegiate classrooms. In this work, we present SPEERLoom: an open-source, robotic Jacquard loom kit designed to be a tool for interweaving cloth fabrication, mathematics, and engineering to support interdisciplinary learning in the classroom. We discuss the design requirements and subsequent design of SPEERLoom. We also present the results of a pilot study in a post-secondary class finding that SPEERLoom supports hands-on, interdisciplinary learning of math, engineering, and textiles. 

For nearly two decades, conversational agents have been used to structure group interactions in online chat-based environments. More recently, this form of dynamic support for collaborative learning has been extended to physical spaces using a combination of multimodal sensing technologies and instrumentation installed within a physical space. This demo extends the reach of dynamic support for collaboration still further through an application of what has recently been termed on-device machine learning, which enables a portable form of multimodal detection to trigger real-time responses. 

At its core, collaboration is about bringing diverse perspectives together to create something new. Diversity may arise along a multiplicity of dimensions, leading to some very similar challenges, and other dimension-specific challenges, each of which require discrete skills to address. Interdisciplinary collaboration, while understudied, has particular workplace relevance. This research seeks to understand what is specific to interdisciplinary collaboration as part of a broader agenda to operationalize key underlying skills that enable interdisciplinary collaboration and subsequently assess and support interdisciplinary collaboration, both in the classroom and in the workplace. The aim of this poster presentation is to engage the community in an intellectual exchange about underlying questions to inform work in progress. 

Socially shared regulation (SSRL) has been recognized as a contributing factor to successful collaborative learning. In this paper, we adopted a process-oriented approach to examine how students deliberate for SSRL through different regulatory triggers in a collaborative learning context. More specifically, this study examines the relationship between different types of regulatory and deliberative characteristics of interactions and then explores their sequential patterns through cognitive and emotional triggers. The study involved ten triads of secondary students (N=30) working on a collaborative learning task. The process mining results showed that following regulatory triggers, groups switched to more metacognitive and socio-emotional interactions as they adopted control strategies, such as defining problems, establishing strategies, and providing social support. This study not only contributes to a better understanding of SSRL by exploring learners’ deliberative negotiation but also presents a novel fine-grain video analysis approach to examine SSRL in collaborative learning. 

Our work aims to increase the collaborative ability of college students in computer science classrooms where students must work towards a shared goal with peers from different backgrounds and abilities. Our work focuses specifically on leveraging high-quality collaborative design to bridge the gap between fiber arts and robotics by enlightening students to their shared foundations in mathematics and computational thinking. We achieve this goal through the design of SPEERLoom (Semi-automated Pattern Executing Educational Robotic Loom), a new open-source Jacquard loom kit designed to foster students' exploration of weaving, mechatronics, mathematics, and computational thinking. In this demonstration we present SPEERLoom and allow the exploration of a sample lesson using the loom.