Using illustrative teaching case studies, this book demonstrates how teaching informed by a learning theory, specifically Variation theory, can equip teachers to facilitate possibilities for students’ learning in effective and powerful ways. For a long period of time teaching has been “black-boxed”, in favour of other explanations of why students learn or not, such as motivation and social interaction.

A large amount of research on teaching and learning, not the least made using Variation theory, has shown that students often need to experience the same aspects of the focused content or capability in order to learn, indicating that relationships between teaching and learning are not unique or even qualitatively different for every individual and every situation. This perspective on the relationship between teaching and learning emphasizes content-specific aspects and in that sense structural components of teaching, while other aspects of schooling such as social interaction and general well-being recede into the background.

The authors argue for the importance of this in the direct development of teachers' independent collective professional knowledge about teaching, and the leverage this gives for developing student learning. They introduce theoretical tools to help teachers to increase the probability that teaching focusing a specific content or capability is predictive of students learning of that specific content or capability, while decreasing contextual dependency without assuming that teaching and learning have a one-to-one relationship.

Intended for teachers, graduate students in education, teacher educators, student teachers, and researchers, this book shows that while there is no simple equation between teaching and learning, there are general, though content specific, aspects of teaching that can be systematically planned and analyzed and used to improve the quality of student learning. The Open Access version of this book, available at www.

taylorfrancis. com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.

0 license. .