计算机科学
机器学习
人工智能
可用的
课程
卷积神经网络
多学科方法
多媒体
教育学
心理学
社会科学
社会学
作者
Tapani Toivonen,Ilkka Jormanainen,Juho Kahila,Matti Tedre,Teemu Valtonen,Henriikka Vartiainen
标识
DOI:10.1109/icalt49669.2020.00099
摘要
Artificial intelligence and machine learning are making their ways rapidly to K-12 education. Google Teachable Machine, powered by convolutional neural networks, provides an easy-to-use yet powerful tool for classification tasks. We conducted a series of co-design workshops with primary school children, where they explored and designed their own machine learning powered applications with Google Teachable Machine. Our results show that Google Teachable Machine is a feasible tool for K-12 education. The trained machine learning models are lightweight and computationally efficient, and the applications are usable even with low-end mobile devices. The students and teachers appreciated the multidisciplinary and inclusive workshop, which supports development of transversal competencies in accordance to the national primary school curriculum.
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