Deep Learning and Subject Teaching

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deep learning
deep learning and curriculum
English deep learning
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Foreign languages deep learning
forthcoming
Geography deep learning
History deep learning
Maths deep learning
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subject deep learning

Product details

  • ISBN 9781032954592
  • Dimensions: 156 x 234mm
  • Publication Date: 13 Oct 2026
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Educators, and policymakers around the world are increasingly keen that students of all levels (school and university alike) develop 'deep learning’. But, what counts as deep learning and what does it look like in different disciplines?

This book examines the issue of students developing ‘deep learning’ in different disciplines including mathematics, science, English, geography, history, religious education, and foreign languages. Each chapter of this essential resource focuses on a particular discipline, clearly articulate its own perspective on deep learning, and discuss a concept or approach that is considered important for deep learning in the particular discipline but whose place in typical teaching practice and/or policy debate tends not to reflect that importance.

A commentary chapter draws out and highlights unifying cross-disciplinary aspects of deep learning making this a must read for all researchers, teacher educators and policy makers interested in deep learning across the disciplines.

Gabriel J. Stylianides is a Professor of Mathematics Education and Fellow of Worcester College at the University of Oxford, UK.

Sibel Erduran is a Professor of Science and Fellow of St Cross College at the University of Oxford, UK.