Computer Science Education

Drawing together the most up-to-date research from experts all across the world, the second edition of Computer Science Education offers the most up-to-date coverage available on this developing subject, ideal for building confidence of new pre-service and in-service educators teaching a new discipline. It provides an international overview of key concepts, pedagogical approaches and assessment practices.
Highlights of the second edition include:
- New sections on machine learning and data-driven (epistemic) programming
- A new focus on equity and inclusion in computer science education
- Chapters updated throughout, including a revised chapter on relating ethical and societal aspects to knowledge-rich aspects of computer science education
- A new set of chapters on the learning of programming, including design, pedagogy and misconceptions
- A chapter on the way we use language in the computer science classroom.
The book is structured to support the reader with chapter outlines, synopses and key points. Explanations of key concepts, real-life examples and reflective points keep the theory grounded in classroom practice.
The book is accompanied by a companion website, including online summaries for each chapter, 3-minute video summaries by each author and an archived chapter on taxonomies and competencies from the first edition.

Sue Sentance is Chief Learning Officer at the Raspberry Pi Foundation and Director of the Raspberry Pi Computing Education Research Centre at the University of Cambridge, UK.
Erik Barendsen is Professor of Computer Science Education at the Open University, the Netherlands, and Professor of Science Education at Radboud University, the Netherlands.
Carsten Schulte is Professor of Computer Science Education at the University of Paderborn, Germany, and Head of the Computer Science Education Research Group.
Nicol R. Howard is Associate Professor and Associate Dean of Academic Affairs in the School of Education at the University of Redlands, USA. She is also co-director of the Race in Education Analytics Learning Lab (REAL Lab).