MATHEMATICAL MODELS OF LEARNING ANALYTICS FOR MASSIVE OPEN ONLINE COURSES E. Sinitsyn, A. Tolmachev, V. Larionova, A. Ovchinnikov Ural Federal University (RUSSIAN FEDERATION) Abstract Online education is rapidly developing in Russia and in the world over the last decades. One of the most popular and available online learning technologies is massive open online courses (MOOC), which are successfully used in the university, school, continuing professional education and informal life-long learning. Nowadays the number of MOOC learners in the world is estimated in tens of millions of people. Nevertheless, using MOOC in the educational process has both advantages and disadvantages. The latter include problems connected with individualization of training, assessment of progress and support for students, and assessment of the quality of courses. Analyzing and predicting the success of students is an important task and tool for solving them. The paper is devoted to developing a model of students’ progress forecasting, which is based on the information theory and allows one to estimate the probability of students’ success in the of the final test when observing the current performance of students. The probabilistic model for group forecasting of the final performance, considered in the paper, allows predicting the distribution of students’ final scores and reduce the level of uncertainty in online learning using MOOCs. It can be useful for assessing the quality of the course as a whole and developing measures to improve it. The statistical model for personalized performance prediction allows one to make a forecast of the final progress for each student and can be used to identify negative trends and problems of students in learning, provide them with relevant feedback and necessary support. The application of the models is considered on the example of an engineering online course created by the Ural Federal University Keywords: MOOC, learning analytics, probabilistic and statistical methods, information theory, learning performance forecasting. 1 INTRODUCTION Online learning has been rapidly developing in Russia and in the world over the last decades. It is used in the educational process of higher educational institutions, corporate universities, schools and organizations implementing professional training programs. One of the most popular and available online learning technologies is massive open online courses (MOOC) [1]. According to Holon IQ [2] nowadays MOOCs have over 80 million learners in the world. By 2030, the number of graduates from schools and universities will increase by about a billion people compared with 2015, which will significantly increase the demand for both traditional and online forms of education. In the global open education market, the leaders among MOOCs platforms are Coursera, Udacity, edX, Udemy, and others. They offer more than ten thousand online courses including: online courses from the best universities in the world; professional courses for additional training, which are recognized by the world's largest corporations; corporate courses for employees of partner companies; general courses for life-long learning [3]. They can be a part of full bachelor or master degree programs on some specific specialisms or single online programs, which can last from a few months to 3–7 hours (mini-format) or 2 hours (micro-format). An example of Russian open education platforms can be viewed on the Open Education Platform (https://openedu.ru/) [4]. To date, 353 online courses from 14 leading Russian universities, including Moscow State University, Moscow Institute of Physics and Technology, Ural Federal University, Higher School of Economics, and others are presented on the platform [5]. Students of these universities, other Russian universities, and anyone wishing to study can the take courses and obtain credits for them [6]. Online learning has both advantages and disadvantages. The advantages include: financial affordability, which is expressed in lower costs as compared with the traditional education; availability of content, which gives the opportunity for distance study form any place at any time; adaptability, which enables students to choose courses from the best teachers, as well as the pace and form of learning; the presence of a large amount of data on the activity of students during the course, Proceedings of EDULEARN19 Conference 1st-3rd July 2019, Palma, Mallorca, Spain ISBN: 978-84-09-12031-4 4395