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Scaffolding Design to Bridge the Gaps between Machine Learning and Scientific Discovery for K-12 STEM Education

Published: 24 June 2021 Publication History

Abstract

Machine Learning (ML) can provide an advanced lens for K-12 students to get their hands on intriguing patterns from real-world data and has the potential to empower young learners with more challenging cognitive skills needed for iterative scientific investigation. However, few efforts have been taken to unearth the unique challenges to engage K-12 teachers and students in ML-empowered scientific discovery (SD) learning. Moreover, it is under-explored what scaffolding can be designed to mitigate the challenges. Based on our previous study and literature research, we identified three gaps for novice learners to conduct ML-empowered SD: (1) cognitive overload in ML visual analytics; (2) insufficient synthesis of multivariate patterns for hypothesis development; (3) the lack of evidence evaluation during the iterative investigation. We also propose three corresponding scaffolding components and evaluation studies for the next step.

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cover image ACM Conferences
IDC '21: Proceedings of the 20th Annual ACM Interaction Design and Children Conference
June 2021
697 pages
ISBN:9781450384520
DOI:10.1145/3459990
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 24 June 2021

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  1. K-12 Education
  2. Machine Learning
  3. Scaffolding Design
  4. Scientific Discovery Learning

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IDC '21
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IDC '21: Interaction Design and Children
June 24 - 30, 2021
Athens, Greece

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Overall Acceptance Rate 172 of 578 submissions, 30%

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Interaction Design and Children
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