The Machine Learning Machine: A Tangible User Interface for Teaching Machine Learning

Kaspersen, Magnus Høholt; Bilstrup, Karl-Emil Kjær; Petersen, Marianne Graves

doi:10.1145/3430524.3440638

Cited by 63 publications

(9 citation statements)

References 28 publications

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“…(Huang et al, 2021) An educational project aiming at the development and implementation of a professional, standardized, internationally accepted system for training and certifying educators and young people in AI (including ML) and Robotics. (Kaspersen et al, 2021) The Machine Learning Machine: a tangible user interface for teaching Machine Learning.…”

Section: No Of Relevant Results (Without Duplicates)mentioning

confidence: 99%

“…(Santana et al, 2018;Bhatia ,2020) Teaching model training using appropriate technological support, enables students to train different ML models and also allows them to observe as well as to describe the process by which models use data to create representations and learn from patterns. (Kaspersen et al, 2021;Bhatia, 2020) 3.2. Pedagogy: What are the findings related to pedagogy adopted for teaching ML in High School?…”

Section: Positive Contributionsmentioning

confidence: 99%

“…An interactive strategy also seems to offer a fertile space for young people to begin to develop intuitions, curiosities, and theories about ML. (Bhatia, 2020;Kaspersen et al, 2021;Sperling and Lickerman, 2012;Van Brummelen et al, 2020;Santana et al, 2018;Wan et al, 2020;Zimmermann-Niefield et al, 2019) High-order thinking tasks help in engaging students with technology. (Voulgari et al, 2021) Students enjoyed project-based learning, building AI systems and had positive feelings about their projects.…”

Section: Positive Contributionsmentioning

confidence: 99%

“…(Wan et al, 2020) The tangible ML machine (MLM), a learning tool that enables K-12 students to iteratively work on ML models for binary classifications of doodles they draw using pen and paper, was found easy and engaging to use allowing students also to quickly find workarounds for any issues preventing any frustration. (Kaspersen et al, 2021)…”

Section: Advantagesmentioning

confidence: 99%

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Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

Martins¹,

Wangenheim²

2022

Informatics in Education

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Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.

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Section: No Of Relevant Results (Without Duplicates)mentioning

confidence: 99%

Section: Positive Contributionsmentioning

confidence: 99%

Section: Positive Contributionsmentioning

confidence: 99%

Section: Advantagesmentioning

confidence: 99%

See 2 more Smart Citations

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

Martins¹,

Wangenheim²

2022

Informatics in Education

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“…Besides completely focusing on interacting with tangibles, they might overlook or skip over other useful information, e.g., text introduction or dialogue displayed on screen [76]. Kaspersen et al [67] found even though TUIs made the machine learning concept less abstract, participants still struggled to understand the model artefact's representation and functionality. They found was a challenge to connect the machine learning process with real-life applications.…”

Section: Was Tui Really Effective or More Effective Than Other Interf...mentioning

confidence: 99%

A Meta-Analysis of Tangible Learning Studies from the TEI Conference

Liang

Preissing

et al. 2022

Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction

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Tangible learning has received increasing attention. However, in the recent decade, it has no comprehensive overview. This study aimed to fill the gap and reviewed 92 publications from all the TEI conference proceedings (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021). We analysed previous studies' characteristics (e.g., study purpose and interactive modalities) and elaborated on three common topics: collaborative tangible learning, tangibles' impacts on learning, and comparisons between tangibles and other interfaces. Three key findings were: (1) Tangibles impacted learning because it could scaffold learning, change learning behaviour, and improve learning emotion; (2) We should see the effectiveness of tangibles with rational and critical minds. (3) Some studies emphasised too much on the interaction of tangibles and ignored their metaphor meanings. For future work, we suggest avoiding an intensive cluster on collaboration and children and consider other valuable areas, e.g., tangibles for teachers, tangibles' social and emotional impacts on students, tangible interaction's meaning and metaphor. CCS CONCEPTS• Applied computing → Education; • Human-centered computing → Human computer interaction (HCI).

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The Landscape of AI Literacy

Leung

et al. 2022

AI Literacy in K-16 Classrooms

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The Machine Learning Machine: A Tangible User Interface for Teaching Machine Learning

Cited by 63 publications

References 28 publications

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

A Meta-Analysis of Tangible Learning Studies from the TEI Conference

The Landscape of AI Literacy

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