Abstract:

E-learning has revolutionized education all over the world, defining a different and promising aspect of education, reinforcing the perception that it builds inclusive knowledge societies. Higher Education Institutes (HEI) adopted this innovative education model in order to provide to their students the option of distance education. Since the most important component of e-learning is e-content, its development is a popular research topic in the educational community. Due to the fact that e-content reusability can be increased by using an approach based on Learning Objects (LOs), many methodologies of e-content design introduce guidelines for creating LOs. Learning Objects can make the process of e-learning effective and can offer high quality e-Learning experience to students. The Hellenic Open University (HOU) is introducing LOs in the educational process, using teaching subject domain ontologies to describe them. The ontologies provide a simple way of identifying the domains covered by a Learning Object, while facilitating its reusability. The preliminary step to create these domain ontologies is the design of the Concept Map (CM), that is a diagram for representing knowledge in a structured form. Concept Maps foster meaningful learning and serve both as a knowledge base for building domain ontologies and as a frame for composing more detailed LOs. Concept Map Mining (CMM), a process for the automatic or semi-automatic creation of Concept Maps from documents, is used to facilitate the construction and sharing of Concept Maps. In this paper, we propose a methodological framework and a semi-automatic method for Concept Map creation from unstructured text, which can even handle the morphologically rich Greek language skillfully. The proposed approach combines language processing tools and the knowledge of domain experts. In addition, a case study based on a HOU teaching domain is presented, illustrating the process of Concept Map Mining and showing encouraging results.

Keywords:

E-content, e-learning, Concept Maps, Concept Map Mining.