Identification Features of External and Internal Variables in the Mathematical Model of Educational Trajectory
Krupa Tatiana
Krupa Tatiana*, GlobalLab, LLC, Moscow, Russia.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2908-2911 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5002029320/2020©BEIESP | DOI: 10.35940/ijeat.C5002.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The article describes the main stages of identification and formation of variables which are included in the developed mathematical model of the student’s educational trajectory. Analyzed are such groups of external variables as geographical location, age and gender, learning style and parameters of user interaction with the user interface, academic performance, level of possession of complex skills. The numerical values of the variables in different conditions are determined. The types of sessions in which information is collected are highlighted. Data arrays from such information sources as the GlobalLab online platform and the electronic diary Diary.ru were analyzed. Using the studied variables, a mathematical model of the student is presented, which takes into account many properties. The aim of the project is to create a technological model for the application of machine learning methods to predict the optimal educational trajectory of the student. Achieving this goal and using the scientific and technical results of the Project will provide a number of useful technical, technological and technical and economic effects.
Keywords: Mathematical model, machine learning, online training, GlobalLab, educational trajectory