Towards Augmented Learning in Science and Engineering in Higher Education

Reference books and encyclopedic knowledge bases present learners with an important source of fundamental concepts. However, the resulting knowledge acquisition process is often hindered by the linearity that is inherent to these resources, making it difficult for learners to realize the many links that exist between these concepts. This research project aims at establishing and implementing a rich semantic model for the identification and classification of knowledge in a core-periphery structure at the service of graduate level students of engineering sciences. This model will allow learners to obtain a global view of knowledge domains and to identify roadmaps for the knowledge acquisition process.