Intercontinental evidence on learners' differentials in sense-making of machine learning in schools

被引:9
|
作者
Sanusi, Ismaila Temitayo [1 ]
机构
[1] Univ Eastern Finland, Sch Comp, Kuopio, Finland
来源
PROCEEDINGS OF 21ST KOLI CALLING CONFERENCE ON COMPUTING EDUCATION RESEARCH, KOLI CALLING 2021, | 2021年
关键词
machine learning education; datasets; neural networks; phenomenography; activity theory;
D O I
10.1145/3488042.3490514
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Given the importance of machine learning for K-12 levels, finding out ways to communicate the concept to students such that it will be less intimidating is necessary. This will help to demystify machine learning for students. Drawing on qualitative methodology approach, this study aims to explore how to teach machine learning in K-12 context using middle school students' samples in Nigeria, Finland, and United States. Considering the cross-contextual approach and the study aim, this research will be a valuable addition to the limited evidence that support differentials in students learning of machine learning technology across culture and background. The study outcome will be an indispensable resource for addressing how the uniqueness of each contexts can be leveraged on to best introduce machine learning to schools.
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页数:2
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