CASPIAN JOURNAL
MANAGEMENT AND HIGH TECHNOLOGIES
Online education system efficiency improvement by the means of machine learning and blockchain technology
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Read | Zhukova L. V., Kiryushina A. A., Kovalchuk I. M., Ruzaeva A. V. Online education system efficiency improvement by the means of machine learning and blockchain technology // Caspian journal : management and high technologies. — 2018. — №2. — pp. 56-68. |
Zhukova L. V. - Senior Lecturer, Business Analyst, National Research University of Higher School of Economics, 20 Myasnitskaya St., Moscow, 101000, Russian Federation, lvzhukova@ec-leasing.ru
Kiryushina A. A. - Cand. Sci. (Economics), Senior Business Analyst, JSC “EC-leasing”, 125 Warshavskoye Shosse, 1 Building, Moscow, 117405, Russian Federation, akiryushina@ec-leasing.ru
Kovalchuk I. M. - business analyst, JSC “EC-leasing”, 125 Warshavskoye Shosse, 1 Building, Moscow, 117405, Russian Federation, ikovalchuk@ec-leasing.ru
Ruzaeva A. V. - Human Resources Manager (HR Manager), ABBYY Company, 127273, Moscow, 2B Otradnaya St., 6 Building, Business Center “Otradny”, Russian Federation, ruzaeva.95@gmail.com
The article studies the problems associated with distance education, the reasons for their occurrence and the ways to solve them. The study considers an example of cluster analysis implementation to differentiate those students who got distance education based on machine learning methods; and describes the blockchain technology adapted for the implementation of the control functions of the system of distance education. To obtain clusters for potential online courses users in accordance with their level of conversion during the training a special survey was conducted on the students of the National Research University “Higher School of Economics” by method of machine learning. The survey also allowed to identify the key factors governing the distribution of students into groups of those who successfully/not successfully complete online courses. These data can be used to forecast the progress of certain students in training, to decide which students to recommend for distance training and - potentially - to individually adapt the training courses. Following the results of the research, recommendations are given to improve the effectiveness of online courses based on machine learning, blockchain technology and other modern forms of work with data.
Key words: дистанционное образование, машинное обучение, пользователи массовых онлайн курсов, обучение в дистанционной среде, дифференцирование пользователей, кластерный анализ, блокчейн, информационные технологии, прогнозирование, distance education, machine learni