scholarly journals Teaching Machine Learning in K–12 Computing Education: Potential and Pitfalls

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Matti Tedre ◽  
Tapani Toivonen ◽  
Henriikka Vartiainen ◽  
Ilkka Jormanainen ◽  
Teemu Valtonen ◽  
...  
Author(s):  
Christiane Gresse von Wangenheim ◽  
Jean C. R. Hauck ◽  
Fernando S. Pacheco ◽  
Matheus F. Bertonceli Bueno

Author(s):  
Matti Tedre ◽  
Henriikka Vartiainen ◽  
Juho Kahila ◽  
Tapani Toivonen ◽  
Ilkka Jormanainen ◽  
...  

2020 ◽  
Author(s):  
Christiane Gresse von Wangenheim ◽  
Lívia S. Marques ◽  
Jean C. R. Hauck

Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early on including ML concepts in order to help students to understand its potential and limits and empowering them to become creators of intelligent solutions. Therefore, we developed an introductory course to teach basic ML concepts, such as fundamentals of neural networks, learning as well as limitations and ethical concerns in alignment with the K-12 Guidelines for Artificial Intelligence. It also teaches the application of these concepts, by guiding the students to develop a first image recognition model of recycling trash using Google Teachable Machine. In order to promote ML education, the interactive course is available online in Brazilian Portuguese to be used as an extracurricular course or in an interdisciplinary way as part of science classes covering recycling topics.


2021 ◽  
pp. 0013189X2110579
Author(s):  
Yasmin B. Kafai ◽  
Chris Proctor

Over the past decade, initiatives around the world have introduced computing into K–12 education under the umbrella of computational thinking. While initial implementations focused on skills and knowledge for college and career readiness, more recent framings include situated computational thinking (identity, participation, creative expression) and critical computational thinking (political and ethical impacts of computing, justice). This expansion reflects a revaluation of what it means for learners to be computationally-literate in the 21st century. We review the current landscape of K–12 computing education, discuss interactions between different framings of computational thinking, and consider how an encompassing framework of computational literacies clarifies the importance of computing for broader K–12 educational priorities as well as key unresolved issues.


2021 ◽  
pp. 366-391
Author(s):  
Christiane Gresse von Wangenheim ◽  
Leonardo P. Degering ◽  
Fernanda Mioto ◽  
Lúcia H. Martins-Pacheco ◽  
Adriano F. Borgatto ◽  
...  

2020 ◽  
Vol 34 (09) ◽  
pp. 13397-13403
Author(s):  
Narges Norouzi ◽  
Snigdha Chaturvedi ◽  
Matthew Rutledge

This paper describes an experience in teaching Machine Learning (ML) and Natural Language Processing (NLP) to a group of high school students over an intense one-month period. In this work, we provide an outline of an AI course curriculum we designed for high school students and then evaluate its effectiveness by analyzing student's feedback and student outcomes. After closely observing students, evaluating their responses to our surveys, and analyzing their contribution to the course project, we identified some possible impediments in teaching AI to high school students and propose some measures to avoid them. These measures include employing a combination of objectivist and constructivist pedagogies, reviewing/introducing basic programming concepts at the beginning of the course, and addressing gender discrepancies throughout the course.


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