ОЦЕНКА КАЧЕСТВА МАССОВЫХ ОТКРЫТЫХ ОНЛАЙН-КУРСОВ В ТЕРМИНАХ НЕЧЕТКИХ МНОЖЕСТВ

Abstract

The article is devoted to the development of a model and algorithm for evaluating the quality of mass open online courses. The algorithm of such an assessment can be used in elearning on MOOC platforms or in elearning management systems for additional professional education or in vocational educational programs of universities. A comparative analysis of existing models, algorithms and methods for evaluating the quality of online courses has been carried out, which indicates a fairly small number of studies in the field of elearning. The choice of quality indicators of mass open online courses is justified, their structure in three dimensions is determined, taking into account the quality of content, course services and student achievements. The quality indicators are systematized, allowing to form a hierarchical graph, on the basis of which an integral indicator of the quality of mass open online courses in terms of fuzzy sets on a threelevel classifier is developed. For an integrated quality assessment, a methodology for assessing the consumer quality of information systems based on fuzzy sets is used. To check the quality of the indicators, the relative Hamming distance between the membership functions is used, which has a trapezoidal shape. The calculation of the quality assessment is given using the example of the online course "Data Analysis in python" in the Moodle elearning management system. To demonstrate the costeffectiveness of online courses, the cost calculation of similar courses in online and offline format was carried out and statistical processing of the results was performed. The purpose of the development is to increase the effectiveness of elearning through the use of a model and an algorithm for evaluating the quality of online courses. This algorithm allows you to determine the quality of a course for a specific MOOC platform or elearning management system and develop an action plan to reduce the number of lowquality online courses. The fuzzy model and algorithm can be integrated into an educational platform to automate the quality assessment of MOOCs and recommend online courses to the student.