[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127916-en":3,"doc-seo-127916-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127916,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",6,"Technology","Mix & Match Machine Learning - An Ideation Toolkit to Design Machine Learning-Enabled Solutions","Machine learning (ML) creates broad opportunities for designers, yet design education often fails to equip learners to ideate with ML knowledge and to apply it effectively in practice. This work proposes the Mix & Match Machine Learning toolkit, combining tangible tokens and a web interface to support ideation. Tokens encode data types and ML capabilities, enabling designers to explore, understand, combine, and operationalize ML while recognizing limitations. Evaluation across two workshops with design students showed support for both learning and ideation goals.","Mix and Match Machine Learning  \nCitation for published version (APA):  \nJansen, A. , & Colombo, S. (2023) . Mix and Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions. In TEI '23: Proceedings of the Seventeenth International Conference on Tangible, Embedded, and Embodied Interaction Article 8 Association for Computing Machinery, Inc.  \n[https://doi.org/10.1145/3569009.3572739](https://doi.org/10.1145/3569009.3572739)  \nDocument license:  \nCC BY-NC-SA  \nDOI:  \n10.1145/3569009.3572739  \nDocument status and date:  \nPublished: 26/02/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 13. Mar. 2025  \nMix & Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions  \nAnniek Jansen∗  \n[a.jansen@tue.nl](a.jansen@tue.nl)  \nDepartment of Industrial Design, Eindhoven University of Technology Eindhoven, The Netherlands  \nSara Colombo∗  \n[s.colombo@tue.nl](s.colombo@tue.nl)  \nDepartment of Industrial Design, Eindhoven University of Technology Eindhoven, The Netherlands  \nFigure 1: The sensing board with a data token and an ML capability token, which are part of the Mix & Match ML toolkit  \nABSTRACT  \nMachine 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.  \n∗ Both authors contributed equally to this research.  \nThis work is licensed under a Creative Commons  \nAttr","cbCaifH7V0a0P13O","https://ap.wps.com/l/cbCaifH7V0a0P13O","pdf",24124136,4,1,19,"English","en",105,"# Abstract\n# Introduction\n## Motivation and challenges in ML for UX design\n## Ideation difficulties with ML-based products and services","[{\"question\":\"What problem does the Mix \\u0026 Match Machine Learning toolkit address?\",\"answer\":\"It addresses the gap in design education and practice, where designers struggle to integrate ML knowledge and ideate with ML effectively.\"},{\"question\":\"How does the toolkit support ML ideation without programming expertise?\",\"answer\":\"It uses tangible tokens representing data types and ML capabilities, together with a web interface, allowing designers to explore, combine, and operationalize capabilities without relying on programming or computer science knowledge.\"},{\"question\":\"How was the toolkit evaluated and what were the outcomes?\",\"answer\":\"The toolkit was evaluated in two workshops with design students. Results indicated it supports both learning and ideation goals.\"}]","Mix & Match Machine Learning - An Ideation Toolkit to Design Machine Learning-Enabled Solutions | PDF",1785942924,48,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"mix-match-machine-learning-an-ideation-toolkit-to-design-machine-learning-enabled-solutions","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/technology/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/mix-match-machine-learning-an-ideation-toolkit-to-design-machine-learning-enabled-solutions/127916/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the Mix & Match Machine Learning toolkit address?","Question",{"text":76,"@type":77},"It addresses the gap in design education and practice, where designers struggle to integrate ML knowledge and ideate with ML effectively.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the toolkit support ML ideation without programming expertise?",{"text":81,"@type":77},"It uses tangible tokens representing data types and ML capabilities, together with a web interface, allowing designers to explore, combine, and operationalize capabilities without relying on programming or computer science knowledge.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the toolkit evaluated and what were the outcomes?",{"text":85,"@type":77},"The toolkit was evaluated in two workshops with design students. 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