Mark Crowley – författare

Are you looking for teaching ideas to make your science lessons come alive?

Full of suggestions for exciting practical work to engage children, this book addresses and explains the science behind the experiments, and emphasises the need to engage the learner through minds-on activities. It shows you where to make links to the national curricula in England, Scotland, Wales and Northern Ireland, and it covers the three sciences: chemistry, biology and physics. The detailed subject knowledge helps you grasp key concepts, and there are lots of useful diagrams to illustrate important points.

Experiments include:

- extracting DNA from a kiwi fruit

- capturing rainbows

- the chromatography of sweets

- removing iron from cornflakes

- a plate tectonic jigsaw

These practical activities will provide you with ways to ensure your students respond enthusiastically to science, and the book will also help you develop your subject knowledge and ensure you meet your Qualified Teacher Status (QTS) standards. Perfect reading for Secondary Science PGCE students, as well as those on the Graduate Teacher Programme (GTP), this book is also ideal for non-specialists who are looking for support as they get to grips with the sciences.

Gren Ireson is Professor of Science Education at Nottingham Trent University. Mark Crowley is a Teaching Research Fellow in the Centre for Effective Learning in Science, Nottingham Trent University. Ruth Richards is Subject Strand Leader for the PGCE and Subject Knowledge Enhancement (SKE) courses in Science at Nottingham Trent University, and an examiner for A-level Geology. John Twidle is Subject Leader for the PGCE and MSc Science programmes at Loughborough University.

Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes.

Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes.