Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design Activities

TL;DR

This paper reviews three conceptual approaches to teaching ML in K-12 education, focusing on transparency, ethics, and activity design, and discusses their challenges and future directions.

Contribution

It identifies and compares three approaches to ML education, emphasizing transparency, ethics, and integration of learner interests, providing a framework for future educational activity design.

Findings

Data-driven approach emphasizes creating datasets and testing models.

Learning algorithm-driven approach focuses on understanding ML algorithms.

Integrated approaches combine data and algorithm perspectives with ethics.

Abstract

In this conceptual paper, we review existing literature on artificial intelligence/machine learning (AI/ML) education to identify three approaches to how learning and teaching ML could be conceptualized. One of them, a data-driven approach, emphasizes providing young people with opportunities to create data sets, train, and test models. A second approach, learning algorithm-driven, prioritizes learning about how the learning algorithms or engines behind how ML models work. In addition, we identify efforts within a third approach that integrates the previous two. In our review, we focus on how the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on learner interests and provide opportunities for designing applications, (3) integrate ethics and justice. In the discussion, we address the challenges and opportunities of current approaches and suggest future directions for the design of learning activities.