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The LilyPad Arduino: using computational textiles to investigate engagement, aesthetics, and diversity in computer science education

Buechley, Leah, Eisenberg, Mike, Catchen, Jaime, Crockett, Ali·2008·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Abstract

The advent of novel materials (such as conductive fibers) combined with accessible embedded computing platforms have made it possible to re-imagine the landscapes of fabric and electronic crafts--extending these landscapes with the creative range of electronic/computational textiles or e-textiles. This paper describes the LilyPad Arduino, a fabric-based construction kit that enables novices to design and build their own soft wearables and other textile artifacts. The kit consists of a microcontroller and an assortment of sensors and actuators in stitch-able packages; these elements can be sewn to cloth substrates and each other with conductive thread to build e-textiles. This paper will introduce the latest version of the kit; reflect on its affordances; present the results of our most recent user studies; and discuss possible directions for future work in the area of personalized e-textile design and its relation to technology education.

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Metadata

Authors
Buechley, L., Eisenberg, M., Catchen, J., & Crockett, A.
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2008-04-06
Pages
423-432
OpenAlex
W2158519170
Cited by
603
References
0
FWCI
14.69
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: The LilyPad Arduino: using computational textiles to investigate engagement, aesthetics, and diversity in computer science education. https://app.cochat.ai/papers/10.1145/1357054.1357123
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Buechley, L., Eisenberg, M., Catchen, J., & Crockett, A. (2008). The LilyPad Arduino: using computational textiles to investigate engagement, aesthetics, and diversity in computer science education. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/1357054.1357123

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.