Instructors' perceptions about teaching machine learning in high school: a first step

Abstract

In today's world, schools all around the world are beginning to see the need of including machine learning curriculum. Teachers' understandings of machine learning remain largely unexamined, despite the growing interest in the topic. Twelve in-service teachers were asked to provide their early thoughts on how to approach teaching machine learning. We used phenomenographic methods to analyze teachers' perceptions about teaching machine learning in K-12 settings. Twelve high school (grades 1–12) computer science teachers from selected countries participated in semi-structured interviews. There were five main themes that came out of the semi-structured interviews: supporting students in developing their technical knowledge; understanding the concept; focusing on professional development practices; contextualizing instructional resources; and ensuring the sustainability of development goals. Techniques, strategies, and long-term sustainability connected to classroom implementation are all a part of the package here. This study's results suggest that existing machine learning approaches should be taught to in-service teachers. As a key component of integrating machine learning in the classroom, creating resources with teachers and students is critical. When teachers assist students in putting machine learning into context, it may have a tremendous impact on society.