Unplugged Activities in the Context of AI

Lindner, Annabel; Seegerer, Stefan; Romeike, Ralf

doi:10.1007/978-3-030-33759-9_10

Cited by 29 publications

(15 citation statements)

References 13 publications

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“…While research on AI and ML education as a whole research domain is emerging, research focusing specifically on the teachers that would drive the teaching and learning process remains limited. Few research studies include Lindner and Romeike's [8] study that explored teachers' knowledge about AI to derive measures to support computer science teachers in Germany. Lindner and Berges [9] also investigated German teachers' pre-concepts about artificial intelligence.…”

Section: Trends and Gaps In Teaching Machine Learning Researchmentioning

confidence: 99%

“…Some gaps have been identified in previous studies as regards teachers and AI in K-12. Lindner and Romeike [8] found out that teachers perceive a lack of suitable teaching materials and best-practice examples and tools in AI. An international comparison was proposed to offer exciting perspectives insights into the teachers' knowledge about AI and derive measures to support computer science teachers [8].…”

Section: Machine Learning and Teacher Educationmentioning

confidence: 99%

“…Lindner and Romeike [8] found out that teachers perceive a lack of suitable teaching materials and best-practice examples and tools in AI. An international comparison was proposed to offer exciting perspectives insights into the teachers' knowledge about AI and derive measures to support computer science teachers [8]. Opel et al [47] also identified that computer science teachers often have only little reliable knowledge about AI and its social impact on our future.…”

Section: Machine Learning and Teacher Educationmentioning

confidence: 99%

See 2 more Smart Citations

Exploring teachers' preconceptions of teaching machine learning in high school: A preliminary insight from Africa

Sanusi

Oyelere

Omidiora

2022

Computers and Education Open

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Section: Trends and Gaps In Teaching Machine Learning Researchmentioning

confidence: 99%

Section: Machine Learning and Teacher Educationmentioning

confidence: 99%

Section: Machine Learning and Teacher Educationmentioning

confidence: 99%

See 1 more Smart Citation

Exploring teachers' preconceptions of teaching machine learning in high school: A preliminary insight from Africa

Sanusi

Oyelere

Omidiora

2022

Computers and Education Open

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“…Through easy-to-access APIs ML becomes a commodity and agency has shifted from the hands of experts to the hands of undergraduate and graduate students. What is more, unplugged activities can be used to scaffold understanding of the training processes used on data sets to produce classifiers in a range of realistic settings [52].…”

Section: Machine Learning Education In K-12mentioning

confidence: 99%

Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education

et al. 2021

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Over the past decades, numerous practical applications of machine learning techniques have shown the potential of data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.

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“…In their Forward Propagation activity, students act as different neurons in the network, passing messages to student-neurons in later layers, and ultimately classifying the contents of the input image. Linder, et al describe a similar forward propagation exercise as part of their series of AI unplugged activities (Lindner, Seegerer, and Romeike 2019).…”

Section: Related Workmentioning

confidence: 99%

GANs Unplugged

Virtue

2021

AAAI

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With the influx of deepfake and style transfer technology in today's news and social media, everyone is told that these applications are powered by artificial intelligence and deep learning, but too often the explanation of how it works goes no further. Rather than waiting until second semester of computer science grad school to learn about generative adversarial networks (GANs), we propose a classroom activity to introduce GANs to secondary school students. Our GANs Unplugged activity steps outside a traditional classroom environment and forms groups of students that physically act as the various components of a GAN. Students in the Real and Generator (i.e. Fake) groups both draw sketches of animals on index cards, but only the Real group has the secret, specific instructions on how to draw their animals. A third Discriminator group tries to determine if each card it receives came from the Real group or the Fake group. The Discriminator group also makes a single feedback mark on the drawing to suggest how that drawing could be more "real" before passing the card back to the group that drew it. As this game progresses, the Discriminators get better at determining real and fake, that is until the Generators adapt and learn to draw more and more realistic "fake" images. We describe the details of GANs Unplugged and present our experience running this activity at three different artificial intelligence summer camps for high school students.

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Unplugged Activities in the Context of AI

Cited by 29 publications

References 13 publications

Exploring teachers' preconceptions of teaching machine learning in high school: A preliminary insight from Africa

Exploring teachers' preconceptions of teaching machine learning in high school: A preliminary insight from Africa

Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education

GANs Unplugged

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