Application of scientific approaches for evaluation of quality of learning objects in eQNet project

Abstract

The paper is aimed to analyse the application of several scientific approaches, methods, and principles for evaluation of quality of learning objects for Mathematics subject. The authors analyse the following approaches to minimise subjectivity level in expert evaluation of the quality of learning objects, namely: (1) principles of multiple criteria decision analysis for identification of quality criteria, (2) technological quality criteria classification principle, (3) fuzzy group decision making theory to obtain evaluation measures, (4) normalisation requirement for criteria weights, and (5) scalarisation method for learning objects quality optimisation. Another aim of the paper is to outline the central role of social tagging to describe usage, attention, and other aspects of the context; as well as to help to exploit context data towards making learning object repositories more useful, and thus enhance the reuse. The applied approaches have been used practically for evaluation of learning objects and metadata tagging while implementing European eQNet and te@ch.us projects in Lithuanian comprehensive schools in 2010.