The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The Machine Learning Machine: A Tangible User Interface for Teaching Machine Learning
Open accessAbstract
Machine Learning (ML) is often used invisibly in everyday applications with little opportunity for consumers to investigate how it works. In this paper, we expand recent efforts to unfold what students should know about ML and how to design tools and activities allowing them to engage with ML. To do so, we explore how to make processes and aspects of ML tangible through the design of the Machine Learning Machine (MLM); a tangible user interface which enables students to create their own data-sets using pen and paper and to iteratively build and test ML models using this data. Based on insights from the design process and a preliminary pilot study with the MLM, we discuss how a tangible approach to engaging with ML can spur curiosity in students and how the iterative process of improving ML models can encourage students to reflect on the relation between data, model and predictions.
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In the citing paper
“The democratization of current AI technologies encourages students to make intelligent agents and machine learning models without needing to program such as ML-for-kids and Teachable Machine (Kaspersen et al., 2021;Long & Magerko, 2020).”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Kaspersen et al., 2021
In the citing paper
“Through a follow-up interview, they found that children were able to apply their new knowledge of ML to their own life and to think up personally meaningful applications using ML (Kaspersen et al., 2021).”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Kaspersen et al., 2021
In the citing paper
“For example, Kaspersen et al. (2021) evaluated students' AI models and user interface design through collecting and labelling data, and building, testing and evaluating models.”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Kaspersen et al. (2021)
Citations
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Cited by
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205
Metadata
- Authors
- Kaspersen, M. H., Bilstrup, K.-E. K., & Petersen, M. G.
- Affiliations
- Aarhus University (DK)
- Venue
- Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction
- Publisher
- ACM
- Published
- 2021-02-14
- Pages
- 1-12
- OpenAlex
- W3132204078
- Cited by
- 89
- References
- 5
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 53 · 2023: 8 · 2022: 14 · 2021: 1
- FWCI
- 22.18
- Type
- conference paper
- Language
- en
- Open access
- green
- www.scopus.com
- Locations
- International Conference on Tangible, Embedded, and Embodied Interaction (TEI) (publishedVersion)
- Scopus (Elsevier) (submittedVersion, other-oa)
- Funders
- Villum Fonden (28831)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- machine learning education, tangible user interfaces, student curiosity
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- CoChat. (2026). Citation check: The Machine Learning Machine: A Tangible User Interface for Teaching Machine Learning. https://app.cochat.ai/papers/10.1145/3430524.3440638
- The paper
- Kaspersen, M. H., Bilstrup, K.-E. K., & Petersen, M. G. (2021). The Machine Learning Machine: A Tangible User Interface for Teaching Machine Learning. Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction. https://doi.org/10.1145/3430524.3440638
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
