With a significant increase in the number of e-learning resources the issue of quality is of current importance. An analysis of existing scientific and methodological literature shows the variety of approaches, methods and tools to evaluate e-learning materials. This paper proposes an approach based on the procedure for estimating parameters of local factors and receiving the integral index of usability quality of e-learning modules. We present a mathematical model which serves as a basis for the automated procedures for expertise. The use of fuzzy logic allows to reduce greatly the complexity of evaluating the formation of a repository of e-learning modules. The proposed approach is focused on the situation, when the university has amassed a large number of e-learning modules that have to be assessed in terms of ergonomics; is able to use experts in ergonomics and organization of e-learning (the experts can provide, as a rule, qualitative assessment); is limited in resources on the development of special software for evaluation of e-learning modules; is forced by the need to reduce the cost of expertise to be limited to considering only the main quality indicators that have the greatest impact on the ergonomics of e-learning modules. For automation of the ergonomic examination procedures a MatLab system is used, in particular Fuzzy Logic Toolbox.
Application of the well-known mathematical tools and widely used means of processing expert qualitative assessments can significantly reduce the cost of the expertise.
E-learning students are generally heterogeneous and have different capabilities knowledge base and needs. The aim of the Sumy State University (SSU) e-learning system project is to cater to these individual needs by assembling individual learning path. This paper shows current situation with e-learning in Ukraine, state-of-art of development of the adaptive e-learning systems and shows results of SSU research in this area. Nowadays the received solutions are different from the known analogues considering an expanded set of information about the features of a particular student's learning activities (19 indicators are analysed, including indicators of progress such as the level of knowledge and student individual features). The corresponding software solutions are being tested in the SSU e-learning environment.