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In the last decade, HCI researchers have designed and engineered several systems to lower the entry barrier for beginners and support novices in learning hands-on creative maker skills. These skills range from building electronics to fabricating physical artifacts. While much of the design and engineering of current learning systems is driven by the advances in technology, we can reimagine these systems by reorienting the design goals around constructivist and sociocultural theories of learning to support learning progression, engagement across artistic disciplines, and designing for inclusivity and accessibility. This one-day workshop aims to bring together the HCI researchers in systems engineering and learning sciences, challenge them to reimagine the future design of systems of learning creative maker skills, form connections across disciplines, and promote collaborative research in the systems of learning creative skills. 

We present Fabriccio, a touchless gesture sensing technique developed for interactive fabrics using Doppler motion sensing. Our prototype was developed using a pair of loop antennas (one for transmitting and the other for receiving), made of conductive thread that was sewn onto a fabric substrate. The antenna type, configuration, transmission lines, and operating frequency were carefully chosen to balance the complexity of the fabrication process and the sensitivity of our system for touchless hand gestures, performed at a 10 cm distance. Through a ten-participant study, we evaluated the performance of our proposed sensing technique across 11 touchless gestures as well as 1 touch gesture. The study result yielded a 92.8% cross-validation accuracy and 85.2% leave-one-session-out accuracy. We conclude by presenting several applications to demonstrate the unique interactions enabled by our technique on soft objects. 

We present Indutivo, a contact-based inductive sensing technique for contextual interactions. Our technique recognizes conductive objects (metallic primarily) that are commonly found in households and daily environments, as well as their individual movements when placed against the sensor. These movements include sliding, hinging, and rotation. We describe our sensing principle and how we designed the size, shape, and layout of our sensor coils to optimize sensitivity, sensing range, recognition and tracking accuracy. Through several studies, we also demonstrated the performance of our proposed sensing technique in environments with varying levels of noise and interference conditions. We conclude by presenting demo applications on a smartwatch, as well as insights and lessons we learned from our experience.