儿童能理解机器学习概念吗?:打开黑匣子的效果

Tom Hitron, Yoav Orlev, I. Wald, Ariel Shamir, H. Erel, Oren Zuckerman
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引用次数: 94

摘要

机器学习服务被集成到日常生活的各个方面。它们的底层流程通常是黑盒化的,以增加易用性。因此,孩子们缺乏探索这些过程和发展基本心智模式的机会。我们提出了一个手势识别研究平台,旨在通过揭示机器学习的构建模块:数据标记和评估来支持从经验中学习。孩子们利用这个平台进行肢体动作,在采样和评估之间进行迭代。他们的理解在实验前/实验后的设计中进行了测试,在三种情况下:仅发现数据标记的学习活动,仅评估或两者兼而有之。我们的研究结果表明,这两个构建模块对于增强儿童对基本机器学习概念的理解是必不可少的。孩子们能够将他们的新知识应用到日常生活中,包括对个人有意义的应用。我们的结论是,儿童与未被发现的机器学习黑盒子的互动有助于更好地理解他们周围的世界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can Children Understand Machine Learning Concepts?: The Effect of Uncovering Black Boxes
Machine Learning services are integrated into various aspects of everyday life. Their underlying processes are typically black-boxed to increase ease-of-use. Consequently, children lack the opportunity to explore such processes and develop essential mental models. We present a gesture recognition research platform, designed to support learning from experience by uncovering Machine Learning building blocks: Data Labeling and Evaluation. Children used the platform to perform physical gestures, iterating between sampling and evaluation. Their understanding was tested in a pre/post experimental design, in three conditions: learning activity uncovering Data Labeling only, Evaluation only, or both. Our findings show that both building blocks are imperative to enhance children's understanding of basic Machine Learning concepts. Children were able to apply their new knowledge to everyday life context, including personally meaningful applications. We conclude that children's interaction with uncovered black boxes of Machine Learning contributes to a better understanding of the world around them.
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