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Ontological principles bridging familiar mathematical concepts with algorithmic processes

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

The intervention used ontological principles to bridge familiar mathematical concepts with algorithmic processes, serving as the conceptual bridge in the PD design. As the article states, "The intervention used ontological principles to bridge familiar mathematical concepts with algorithmic processes." This framing made machine-learning classification accessible through geometry content educators already knew.

Design Implications

Context

Requirements

  • Learners must have familiar domain concepts (here, geometry) to which algorithmic processes can be bridged

Constraints

  • Applied in the context of geometry-based machine-learning activities for K–12 educators

Target Learners

  • K–12 educators

Learning Goals

  • Understanding machine-learning classification through familiar mathematics

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 ontological-bridging-math-concepts-algorithmic-processes
title Ontological principles bridging familiar mathematical concepts with algorithmic processes
description The intervention used ontological principles to bridge familiar mathematical concepts with algorithmic processes, serving as the conceptual bridge in the PD design.
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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