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

Abstract

The teaching of machine learning is now considered essential and relevant in schools globally. Despite the ongoing discourse and increased research in the emerging field, teachers' conceptions of machine learning remain under-researched. This study aims at filling the gap by describing the initial conceptions of teaching machine learning by 12 African in-service teachers. We detailed the result of a phenomenographic analysis of teachers' pre-conceptions on teaching machine learning in K-12 settings. Twelve high school (Grades 10–12) computer science teachers in some selected African countries were recruited for a semi-structured interview. Five categories emerged from the analysis of the semi-structured interviews as follows: supporting student technical knowledge, having knowledge of the concept, focusing on professional development practices, contextualizing teaching resources and tools, and sustainability for development goals. These involve the relevance of teaching machine learning, the pedagogical approaches, strategies, and sustainability relating to practical implementation in schools. The results suggest the need to train in-service teachers to use existing tools designed for introducing machine learning. The teachers should also be involved in the co-designing process of resources considering contextual factors and, significantly, the curriculum to integrate machine learning into mainstream education. Involving teachers in the development process would help contextualize machine learning, contributing to real impact and societal changes.

Keywords

• Machine learning
• High school
• Africa
• Teacher preconceptions
• Phenomenography