Modelling Changes in Understanding

Eileen Scanlon The main target audience is researchers in science education and cognitive scientists for whom physics learning has been a great source of interest. The relevance of the book to those interested in instructional design is that in the words of the editors, it contributes to an "understanding of the distance between students' conceptions and the knowledge to be taught". It is undoubtedly true that the data presented in the early chapters of the book are of interest. Where the general reader may have more difficulty is in appreciating the contribution of the machine learning approach to understanding human learning, In this Kurt Van Lehn's introductory remarks are helpful, pointing out the different experimental and modelling approaches used in different chapters of the book. However, a remark made in the editors' general overview states that the authors of the book have diverse opinions regarding this relationship between machine and human learning. If this topic interests you, there is a range of approaches on offer here. Computers & Education, Vol 31