Abstract:

The solution of the problem of knowledge modeling is currently based on modern achievements of information technologies, the attraction of ontological engineering and formalization of the concept of multiparadigm approach. In the educational paradigm effectively knowledge representation on the basis of commonality and variability of the reference concepts of the training disciplines content. Thereby it is currently important displaying the pedagogical models and educational resources by using the object and generative paradigms. They have a great set of tools of modeling, design and visualization of the concepts of academic disciplines, as well as methods of modeling the properties and mechanisms of their interaction. We studied the possibility of educational information modeling using ontology models and feature models. An ontology defines the conceptualization that underlies the formalism of the representation and knowledge modeling in such a way that they become suitable for computer processing. Formally ontology is a system consisting of sets of concepts and assertions about these concepts, formulated with the help of feature models. Feature modeling is the main method of identifying and fixing the commonality and variability of concepts in the ontologies and on the feature properties. As a result, it enables the development of reusable educational components for the generation of new disciplines and courses. The process of building ontology of educational resources begins with the definition of the discipline reference concepts and their subsequent specification, where each concept is represented by feature set. For example, one of the reference concepts regarding the knowledge of software engineering SWEBOK - "Identification of requirements" can be represented by a set of following required features: f1 -high-level description of the task; f2 - low-level description of the requirements; f3 - expert assessment and business rules; f4 - system services. In this case, the feature f2 - has an additional level of specificity by required properties f21 - functional requirements and f22 - non-functional requirements and optional feature f23 - inverse requirements and feature f4- optional features: f41 - formulation of constraints on the behavior of a software system and f42 - formulation services based on business rules. Feature model of the considered concept is specified by expression of knowledge, and the visualization of the model is by a graph. The result of the study is the ability and effective use of the paradigms of object and generating programming for the representation and organization of knowledge in educational activities. It is shown that knowledge modeling using ontologies and feature models and use for these purposes, the commonality and variability of concepts enables the generation of new knowledge and as well as the adaptation of disciplines, courses and programs, in accordance with the requirements for professional competences of graduates.

Keywords:

knowledge modeling, paradigm, ontology, commonality and variability.