The distance learning quality assessing methods are divided into static and internal. Static methods are based on learning, comparison of the obtained digital data with each other or with the sample, their generalization, interpretation and the formation of scientific and practical conclusions. Internal methods use the idea of analyzing the received digital data using the built-in (or additional) tools of the system. Perspective in our time is the direction with the application of cognitive maps. The cognitive map is a structure of knowledge, a graphic representation of causal relationships between concepts, attributes, indicators, interacting with systems and their blocks. The closest and most accurate implementation of cognitive maps is neural networks.
The purpose of the presented paper is searching of features that have the greatest impact on the education quality. This paper is devoted to using of convolutional neural networks (CNN) in the synthesis of cognitive maps in order to determine information features that affect the level of student’s education of university when using the component of distance learning in the university's educational process. Synthesis of CNN for the construction of cognitive maps occurs as follows: after creating CNN and connecting it to LMS Moodle, data sets from the LMS Moodle database are submitted to the CNN input. When using the filter, the weight of each data set is formed. All data sets are interrelated with a predetermined set of features considered. Based on the results of operations, a characteristic map is formed - those functions whose weight is greatest are selected. Advantages of CNN - high speed of data processing, automation of the process of calculating the weight for each function, high resistance to interference. The most informative features were identified: the number of tests in the course; availability of practical assignment activities; theoretical materials posted on the course; time spent by the student on the course after authorization. Here are the quality features, which can improve the quality of distance learning in general.