Deep Learning in the Disciplines

Abstract

Deep learning is one of those seductive phrases that promises lofty educational goals and outcomes. It is often defined in contrast to shallow learning, a kind of learning often characterised by rote memorisation that produces recall, recitation, or parroting back of presented content. When pressed to define deep learning, its products are often described as understanding, application, and transfer of knowledge that come about through thinking and reasoning processes. Although many nod their heads in agreement to such characterisations, these terms are as unspecified and vague as the term they are meant to define. Further specificity depends on the disciplinary domain or subject matter area. Deep learning cannot be adequately specified in domain-general ways. Hence, the importance of the mission of this book: to explore definitions of deep learning in a variety of subject matter content areas. Such specification is necessary for educators to provide opportunities for students to engage in deep learning and generate products that can be taken as evidence that deep learning has occurred. Eight of the chapters in the book explore the meaning of deep learning in a specific discipline, two consider what deep learning is when the context is teacher education, and one explores roles for generative AI technologies in supporting teachers’ deep learning in their discipline and in disciplinary pedagogy. This commentary sets a context for remarks about the contributions in this book by first taking up some general issues regarding deep learning and then briefly sketching a framework for conceptualising knowledge and practices integral to deep learning. The commentary concludes by posing some of the key challenges for making deep learning an educational reality.