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Technological advances in computer vision and machine learning image and audio classification will continue to improve and evolve. Despite their prevalence, teachers feel ill-prepared to use these technologies to support their students’ learning. To address this, in-service middle school teachers participated in professional development, and middle school students participated in summer camp experiences that included the use of Google’s Teachable Machine, an easy-to-use interface for training machine learning classification models. An overview of Teachable Machine is provided. As well, lessons that highlight the use of Teachable Machine in middle school science are explained. Framed within Personal Construct Theory, an analysis of the impact of the professional development on middle school teachers’ perceptions (n = 17) of science lessons and activities is provided. Implications for future practice and future research are described.
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Exploring Machine Teaching for Object Recognition with the Crowd
Teachable interfaces can enable end-users to personalize machine learning applications by explicitly providing a few training examples. They promise higher robustness in the real world by significantly constraining conditions of the learning task to a specific user and their environment. While facilitating user control, their effectiveness can be hindered by lack of expertise or misconceptions. Through a mobile teachable testbed in Amazon Mechanical Turk, we explore how non-experts conceptualize, experience, and reflect on their engagement with machine teaching in the context of object recognition.
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- Award ID(s):
- 1816380
- PAR ID:
- 10107172
- Date Published:
- Journal Name:
- Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- LBW0279:1 - LBW0279:6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3290607.3312873
