Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions

Anniek Jansen; Sara Colombo

doi:10.1145/3569009.3572739

Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions

Anniek Jansen, Sara Colombo

Communication and Cognition

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

Machine learning (ML) provides designers with a wide range of opportunities to innovate products and services.
However, the design discipline struggles to integrate ML knowledge in education and prepare designers to ideate with ML. We propose the Mix & Match
Machine Learning toolkit, which provides relevant ML knowledge in the form of tangible tokens and a web interface to support designers’ ideation processes. The tokens represent data types and ML
capabilities. By using the toolkit, designers can explore, understand, combine, and operationalize the capabilities of ML and understand its limitations, without depending on programming or computer science knowledge. We evaluated the toolkit in two workshops with design students, and we found that it supports both learning and ideation goals. We discuss the design implications and potential
impact of a hybrid toolkit for ML on design education and practice.

Original language	English
Title of host publication	TEI 2023 - Proceedings of the 17th International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, Inc, 8. (ACM International Conference Proceeding Series)
Place of Publication	New York
Publisher	Association for Computing Machinery
Number of pages	18
ISBN (Print)	978-1-4503-9977-7
DOIs	https://doi.org/10.1145/3569009.3572739
Publication status	Published - 26 Feb 2023

Keywords

design ideation toolkit
machine learning
tangible user interface
ML capabilities
data types
design education

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10.1145/3569009.3572739Licence: CC BY-NC-SA

Mx & MatchFinal published version, 22.9 MBLicence: CC BY-NC-SA

Cite this

Jansen, A., & Colombo, S. (2023). Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions. In TEI 2023 - Proceedings of the 17th International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, Inc, 8. (ACM International Conference Proceeding Series) Association for Computing Machinery. https://doi.org/10.1145/3569009.3572739

Jansen, Anniek ; Colombo, Sara. / Mix & Match Machine Learning : An Ideation Toolkit to Design Machine Learning-Enabled Solutions. TEI 2023 - Proceedings of the 17th International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, Inc, 8. (ACM International Conference Proceeding Series). New York : Association for Computing Machinery, 2023.

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abstract = "Machine learning (ML) provides designers with a wide range of opportunities to innovate products and services. However, the design discipline struggles to integrate ML knowledge in education and prepare designers to ideate with ML. We propose the Mix & MatchMachine Learning toolkit, which provides relevant ML knowledge in the form of tangible tokens and a web interface to support designers{\textquoteright} ideation processes. The tokens represent data types and MLcapabilities. By using the toolkit, designers can explore, understand, combine, and operationalize the capabilities of ML and understand its limitations, without depending on programming or computer science knowledge. We evaluated the toolkit in two workshops with design students, and we found that it supports both learning and ideation goals. We discuss the design implications and potentialimpact of a hybrid toolkit for ML on design education and practice.",

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Jansen, A & Colombo, S 2023, Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions. in TEI 2023 - Proceedings of the 17th International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, Inc, 8. (ACM International Conference Proceeding Series). Association for Computing Machinery, New York. https://doi.org/10.1145/3569009.3572739

Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions. / Jansen, Anniek; Colombo, Sara.
TEI 2023 - Proceedings of the 17th International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, Inc, 8. (ACM International Conference Proceeding Series). New York: Association for Computing Machinery, 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Jansen A, Colombo S. Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions. In TEI 2023 - Proceedings of the 17th International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, Inc, 8. (ACM International Conference Proceeding Series). New York: Association for Computing Machinery. 2023 doi: 10.1145/3569009.3572739

Mix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions

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Keywords

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