Students interdisciplinary knowledge estimation with analysis of his (Her) behavior in social network: Ontological approach

Abstract

The paper considers task of quantitative estimation of the student’s interdisciplinary knowledge. A set of estimation methods based on subject ontology usage formalized as a semantic network is been proposed. The following machine learning types were used: supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, active learning, multi-level learning, multitasking learning. The prototype of a software system that extracts information about the activity of students in social networks and evaluates their interdisciplinary knowledge with the use of these methods is being presented.