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Makerspaces persist as formal and informal spaces of learning for youth, promoting continued interest in studying how design can support the variety of learning opportunities within these spaces. However, much of the current research examining learning in makerspaces neglects the perspectives of educators. This not only hinders our understanding of educators’ goals and how educators navigate makerspaces but also constrains how we frame the design space of the learning experiences and environments. To address this, we engaged in a set of semi-structured interviews to examine the contexts, goals, values, and practices of seven educators across five makerspaces. A thematic analysis of the data identified six key categories of competencies that these educators prioritize including a range of skills, practices, and knowledge, such as technical proficiency, communication, and contextual reflection. The analysis also identified five categories of strategies to accomplish certain goals, such as scaffolding, collaboration, and relationship building. Last, it also shed light on three categories of challenges faced at the student-level, teacher-level, and institutional level. We conclude with a discussion on our insights into how we can broaden the problem space in the design of educational technologies to support learning in makerspaces. 

A recent study on motor-skill training showed that adaptive training tools that use shape-change to adapt the training difficulty based on learners’ performance can lead to higher learning gains. However, to date, no support tools exist to help designers create adaptive learning tools. Our formative study shows that developing the adaptive learning algorithm poses a particular challenge. To address this, we built Adapt2Learn, a toolkit that auto-generates the learning algorithm for adaptive tools. Designers choose their tool’s sensors and actuators, Adapt2Learn then configures the learning algorithm and generates a microcontroller script that designers can deploy on the tool. Once uploaded, the script assesses the learner’s performance via the sensors, computes the training difficulty, and actuates the tool to adapt the difficulty. Adapt2Learn’s visualization tool then lets designers visualize their tool’s adaptation and evaluate the learning algorithm. To validate that Adapt2Learn can generate adaptation algorithms for different tools, we built several application examples that demonstrate successful deployment.