Pedagogies of Machine Learning in K-12 Context

This research Full paper presents the pedagogies of machine learning in K-12. The new learning pedagogies and technologies are introduced with the aim of enhancing student engagement, experience and learning outcome. This study examined how machine learning has been taught in the recent past and further explores the ways and suitable approaches for K-12 context. Literatures on pedagogies associated with machine learning were reviewed to understand the dynamics and suitability of these pedagogies to support machine learning teaching. Though studies have explored pedagogies for machine learning in higher education context, few studies explored pedagogical strategies for teaching machine learning in K-12. In all, the pedagogies employed in teaching and learning of machine learning has not witnessed much research in literature. The pedagogical strategies revealed in the literature are mostly adopted in the higher education institutions to enable the of teaching machine learning concepts. The literature survey revealed several pedagogical strategies such as problem-based learning, project-based learning and collaborative learning used in higher education institutions. The revealed pedagogies suggest learners-centered approaches such as active learning, inquiry-based, participatory learning, design-oriented learning among others will be suitable for teaching machine learning in K-12 settings.