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Introducing Machine Learning with Scratch and Robots as a Pilot Program for K-12 Computer Science Education

Chan-Jin Chung 1 and Lior Shamir 2
1. Department of Mathematics and Computer Science, Lawrence Technological University, Southfield, Michigan, USA
2. Department of Computer Science, Kansas State University, Manhattan, Kansas, USA

Abstract—Machine learning (ML), a branch of artificial intelligence (AI), is a method that enables systems to learn from data for the purpose of recognizing patterns and making decisions without being explicitly programmed. In the past decade machine learning has been growing rapidly, and ML technologies such as speech recognition, spam filters, smart email reply, online recommendations, face recognition, fake news detection, and self-driving cars have become pivotal in modern daily life. However, computer science education has not yet fully adjusted to the tremendous growth in the sub-field of AI. This paper describes an approach of introducing K-12 students to ML through an on-line summer camp. The students are introduced to the concept of ML by hands-on activities of developing applications for recognizing text, numbers, sounds, images, and video data using a web-based cloud service tool "Machine Learning for Kids" and Scratch 3 programming language combined with Lego Mindstorms EV3 robots. The results show that the tools and technologies used in the camp are suitable for K-12 students, also when used in the form of online training. Pre and post surveys show that students express basic knowledge in ML and higher interest in coding and STEM after being exposed to the proposed training.

Index Terms—artificial intelligence, computational thinking, computer science education, k-12 STEM education, online learning, machine learning, robots, scratch coding

Cite: Chan-Jin Chung and Lior Shamir, "Introducing Machine Learning with Scratch and Robots as a Pilot Program for K-12 Computer Science Education," International Journal of Learning and Teaching, Vol. 7, No. 3, pp. 181-186, September 2021. doi: 10.18178/ijlt.7.3.181-186

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.