"Now, I Want to Teach It for Real!": Introducing Machine Learning as a Scientific Discovery Tool for K-12 Teachers
"Now, I Want to Teach It for Real!": Introducing Machine Learning as a Scientific Discovery Tool for K-12 Teachers
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“现在，我想真正地教它！”：向 K-12 教师介绍机器学习作为科学发现工具
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Zhengyan Bai
中科院分区：
文献类型：
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作者：
Xiaofei Zhou;Jingwan Tang;Michael Daley;Saad Ahmad;Zhengyan Bai
.机器学习（ML）是一种强大的工具，可以揭示数据中隐藏的模式，挖掘新的见解并促进科学发现（SD）。然而，通常需要专业知识才能充分发挥ML的潜力。很少有人开始用ML指导社会青年，也没有把ML作为K-12年龄段的SD工具。本研究提出了SmileyDiscovery，一个ML授权的学习环境，促进SD的K-12学生和教师。我们与18名K-12 STEM教师进行了2次会议的初步研究。调查结果证实了SmileyDiscovery在支持教师（1）进行ML授权的SD，（2）设计自己的与课程一致的SD教案，以及（3）同时快速理解k均值聚类方面的有效性。从我们的研究中提炼出的设计启示可以应用于未来的系统中，以促进更有效的学习支持。
. Machine Learning (ML) is a powerful tool to unveil hidden patterns in data, unearth new insights and promote scientific discovery (SD). However, expertise is usually required to actualize the potential of ML fully. Very little has been done to begin instructing the youth of society in ML, nor utilize ML as an SD tool for the K-12 age range. This research proposes SmileyDiscovery, an ML-empowered learning environment that facilitates SD for K-12 students and teachers. We conducted a 2-session preliminary study with 18 K-12 STEM teachers. Findings con-firm the effectiveness of SmileyDiscovery in supporting teachers to (1) carry out ML-empowered SD, (2) design their own curriculum-aligned SD lesson plans, and (3) simultaneously obtain a rapid understanding of k-means clustering. Design implications distilled from our study can be applied to foster more effective learning support in future systems.