According to Mizoguchi, Hayashi, and Bourdeau, the authors of one of the papers in this book, an ontology is more than a taxonomy of the target. They propose a practical working definition of ontology: a system of necessary and sufficient concepts that characterize the target in addition to the taxonomy. Robust ontologies are important for teaching and learning. They describe the subject matter, the subject’s place in the curriculum and the course, how instructors present the material to the students and how students approach the subject, and how instruction may be assessed. Ontologies are central to the semantic Web. The emphasis of this book is on the use of semantic Web technologies in e-learning. However, semantic Web technologies are just as useful in face-to-face classrooms and seminar/tutorial settings because of the application of rigorous thought in applying these technologies. Many courses and curricula are hybrid, involving synchronous face-to-face instruction and asynchronous work on the Internet.

The book consists of three principal parts: Part 1, “Ontologies for e-Learning,” Part 2, “Semantic Web Technologies for e-Learning,” and Part 3, “Social Semantic Web Applications.” In the first part, the papers discuss the evolution of ontologies, documenting changes, authoring content, developing tutoring systems, and developing test generation systems based on ontologies. This part includes Lillian Cassel’s very interesting paper on the ontology of computing in curriculum development. Computer science, information systems, information technology, software engineering, and informatics all share a common core, with sufficient distinguishing characteristics in the application domain to differentiate them. Curricula for these areas evolve rapidly, and it is hard to capture the changes. The lessons learned in describing computing curricula are also applicable in other disciplines--for example, distinguishing among chemistry, biochemistry, and materials science, or physical and environmental geology. All disciplines seek to describe themselves, including what students should learn and professors should teach. Each area is a candidate for serious research in ontology engineering.

Part 2 has several papers on applications--for example, capturing and interpreting feedback for instructors; teaching geometry, mathematics, and philosophy; and assessment. “Assessment” is a dreaded concept and procedure, even if one is in an area that is accredited. Assessment is a lot of work, but it is valuable if undertaken with a serious professional attitude and useful data to study. Semantic Web technologies based on ontologies that describe the subject matter and the necessary and sufficient concepts will focus assessment activities on what is important.

The third and shortest--just three papers--part is on social semantic Web applications. A relatively recent and rapidly developing aspect of computing is social networking. These networks have become a significant component of instructional environments.

The papers in this book constitute a good contemporary picture of the state of research on the fundamental and analytical aspects of teaching and learning. Since most of the well-recognized theoretical bases underlying education research come from psychology and sociology, the engineering analytic approach, on which practical constructs in semantic Web technologies are created, is a complementary point of view that is rigorous, synthetic, and testable.