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Authors: Xiaoyu Wan, Xiaofei Zhou, Zaiqiao Ye, Chase K. Mortensen, and Zhen Bai
Published: 21 June 2020 Publication History
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Abstract
There is an increasing need to prepare young learners to be Artificial Intelligence (AI) capable for the future workforce and everyday life. Machine Learning (ML), as an integral subfield of AI, has become the new engine that revolutionizes practices of knowledge discovery. Making ML experience accessible to young learners, however, remains challenging due to its high demand for mathematical and computational skills. This research focuses on designing novel learning environments that help demystify ML technologies for K-12 students, and also investigating new opportunities for maximizing ML accessibility through integration with scientific discovery in STEM education. We developed SmileyCluster - a hands-on and collaborative learning environment that utilizes glyph-based data visualization and superposition comparative visualization to assist learning an entry-level ML technology, namely k-means clustering. Findings from an initial case study with high school students in a pre-college summer program show that SmileyCluster leads to positive change in learning ML concepts, methods and sense-making of patterns. Findings of this study also shed light on understanding ML as a data-enabled approach to support evidence-based scientific discovery in K-12 STEM education.
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Index Terms
SmileyCluster: supporting accessible machine learning in K-12 scientific discovery
Human-centered computing
Human computer interaction (HCI)
HCI design and evaluation methods
User studies
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Published In
IDC '20: Proceedings of the Interaction Design and Children Conference
June 2020
642 pages
ISBN:9781450379816
DOI:10.1145/3392063
- General Chairs:
- Elisa Rubegni
University of Lancaster, UK
, - Asimina Vasalou
University College London, UK
, - Program Chairs:
- Narcís Parés
Universitat Pompeu Fabra, Spain
, - Nitin Sawhney
Aalto University, Finland
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Published: 21 June 2020
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Author Tags
- AI literacy
- STEM education
- data visualization
- hands-on learning
- scientific discovery
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- Research-article
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- National Science Foundation
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Overall Acceptance Rate 172 of 578 submissions, 30%
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https://dl.acm.org/doi/10.1145/3613905.3650762
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https://dl.acm.org/doi/10.1145/3613904.3641930
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