arXiv 2406.034802024-06cs.CY

解构K-12教育中机器学习的学习与教学方法：透明性、伦理与设计活动

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

Luis Morales-Navarro, Yasmin B. Kafai

发表信息：: The 19th WiPSCE Conference on Primary and Secondary Computing Education Research 2024

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AI总结：

本文通过文献回顾提出K-12机器学习教育的三种方法（数据驱动、算法驱动及整合式），并分析其在透明性、学习者兴趣与设计机会、伦理正义方面的表现，为未来学习活动设计提供方向。

摘要：

在这篇概念性论文中，我们回顾了现有关于人工智能/机器学习（AI/ML）教育的文献，以识别学习和教授机器学习可被概念化的三种方法。其中一种数据驱动方法强调为年轻人提供创建数据集、训练和测试模型的机会。第二种学习算法驱动方法优先学习机器学习模型背后的学习算法或引擎如何工作。此外，我们还识别出第三种方法中的努力，该方法整合了前两种方法。在我们的回顾中，我们关注这些方法如何：（1）对机器学习的不同方面进行玻璃盒（glassbox）和黑盒（blackbox）处理，（2）建立在学习者兴趣之上并提供设计应用的机会，（3）整合伦理与正义。在讨论中，我们探讨了当前方法的挑战与机遇，并为学习活动的设计提出了未来方向。

英文摘要：

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.

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