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Visual tools for teaching machine learning in K-12: A ten-year systematic mapping.

Christiane Gresse von Wangenheim1, Jean C R Hauck1, Fernando S Pacheco2

  • 1Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil.

Education and Information Technologies
|May 10, 2021
PubMed
Summary

Visual tools can teach Machine Learning (ML) in schools, preparing students for an AI-driven society. A systematic review identified 16 tools, primarily for image recognition via block-based programming, supporting basic ML model creation and deployment.

Keywords:
Computing educationK-12Machine learningVisual tool

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Area of Science:

  • Computer Science Education
  • Artificial Intelligence Pedagogy

Background:

  • The increasing impact of Artificial Intelligence (AI) necessitates early Machine Learning (ML) education for K-12 students.
  • Developing age-appropriate tools is crucial for fostering comprehensive ML understanding and empowering students as creators of smart solutions.

Purpose of the Study:

  • To systematically map and analyze visual tools for teaching Machine Learning in K-12 education over a ten-year period.
  • To evaluate these tools based on educational characteristics, ML model development/deployment support, and development/evaluation methodologies.

Main Methods:

  • A systematic mapping study was conducted over ten years.
  • Identified and analyzed emerging visual tools supporting ML education in K-12.
  • Evaluated tools on educational features, ML model lifecycle support, and development/validation approaches.

Main Results:

  • 16 visual tools were identified, primarily for extracurricular activities.
  • Tools focus on interactive development of ML models for image recognition using supervised learning.
  • Integration with block-based languages (Scratch, App Inventor) facilitates deployment in games and apps.

Conclusions:

  • Existing visual tools can enhance students' understanding of Machine Learning concepts.
  • Further research is needed on the educational design of these tools for effective school adoption.
  • Enhancements are required to comprehensively support the K-12 ML learning process.