SmileyCluster: supporting accessible machine learning in K-12 scientific discovery
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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Thirteen studies used quantitative methods to assess the knowledge acquisition of K-12 and university students via pre- and post-knowledge tests, and students' perceived abilities.
“Thirteen studies used quantitative methods to assess the knowledge acquisition of K-12 and university students via preand post-knowledge tests (e.g., What are the characteristics of depthfirst search?), and students' perceived abilities (e.g., How would you rate your knowledge about search algorithms?) (Kandlhofer et al., 2016;Wan et al., 2020).”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Wan et al., 2020
Wan et al. conducted pre-post questionnaires with written answers to questions relating to clustering, similarity comparison and k-means clustering process.
“Wan et al. (2020) conducted pre-post questionnaires with written answers to questions relating to clustering, similarity comparison and k-means clustering process”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Wan et al. (2020)
Students' AI knowledge acquisition and retention of AI skills was assessed in Lin et al. (2021), Wan et al. (2020) and Rodríguez-García et al. (2021) studies via some pre-post knowledge tests.
“Students' AI knowledge acquisition and retention of AI skills was assessed in Lin et al. (2021), Wan et al. (2020) and Rodríguez-García, Moreno-León, Román-González, & Robles, 2021 studies via some pre-post knowledge tests.”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Wan et al. (2020)
Cites
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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 · see citing works
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“Other approaches use data visualization and concepts of gamification to engage the student in the learning process (Reyes et al., 2020; Wan et al., 2020) or combine traditional programming activities with ML model building (Rodríguez-García et al., 2020).”
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Explicating AI Literacy of Employees at Digital Workplaces
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A systematic review of teaching and learning machine learning in K-12 education
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Self-citationExploring Generative Models with Middle School Students
Ali, S., DiPaola, D., Lee, I., Hong, J., & Breazeal, C. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445226 · Cited by 82 · see citing works
A Systematic Review of Ability-diverse Collaboration through Ability-based Lens in HCI
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- Authors
- Wan, X., Zhou, X., Ye, Z., Mortensen, C. K., & Bai, Z.
- Affiliations
- University of Rochester (US)
- Indiana University Bloomington (US)
- Utah State University (US)
- Venue
- Proceedings of the Interaction Design and Children Conference
- Publisher
- ACM
- Published
- 2020-06-21
- Pages
- 23-35
- OpenAlex
- W3038022479
- Cited by
- 77
- References
- 5
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 13 · 2023: 11 · 2022: 17 · 2021: 9 · 2020: 1
- FWCI
- 11.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Interaction Design and Children (IDC) (publishedVersion)
- Funders
- National Science Foundation (1659250)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Social Sciences › Education › Science Education and Pedagogy
- Keywords
- machine learning education, K-means clustering, K-12 STEM education, AI literacy, high school students
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