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Unplugged explainable machine-learning activities using feature matrices to classify geometric shapes

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

This pattern has learners create explainable feature matrices to classify geometric shapes, making machine-learning algorithms visible without complex software. The article states participants "created explainable feature matrices to classify geometric shapes, making machine-learning algorithms visible and accessible without requiring complex software or technological tools". It was delivered online within K–12 teacher professional development.

Design Implications

Context

Requirements

  • No complex software or technological tools are required, per the article's description of the activities

Constraints

  • Studied with ten K–12 educators in an online PD context

Target Learners

  • K–12 educators

Learning Goals

  • AI literacy
  • understanding of machine-learning classification processes

Claims

Examples

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Key Sources

  • Sung, W., & Gunpinar, Y. (2026). Bringing Artificial Intelligence Literacy Into Online Education: Machine-Learning Integration Through Geometry in K–12 Teacher Professional Development. The International Review of Research in Open and Distributed Learning, 27(2), 21–45. https://doi.org/10.19173/irrodl.v27i2.9140
Page metadata
Field Value
type design
id unplugged-feature-matrix-shape-classification
title Unplugged explainable machine-learning activities using feature matrices to classify geometric shapes
description This pattern has learners create explainable feature matrices to classify geometric shapes, making machine-learning algorithms visible without complex software.
status review
generated by process:settle-candidates on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 1 — sung-2026

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