Vectorization and Optimization of User Behavior Data in E-Learning Systems
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: 2894-2897 | Volume-9 Issue-3, February 2020. | Retrieval Number: B4503129219/2020©BEIESP | DOI: 10.35940/ijeat.B4503.029320
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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: At the first stage, an applied scientific research developed a procedure for collecting data on the parameters of user interaction with the user interface. This input procedure receives many heterogeneous messages about the actions of a particular user in the interface, while the output represents a vector that describes the user in aggregated form. The set of vectors for different users, in turn, was then used as input for the k-means clustering algorithm, the result of which is the user’s attitude to one of the k clusters that distinguish the user by the type of behavior. User interface interaction data is available to 67.8% of GlobalLab platform users. There is no such data for the Diary.ru electronic diary. Considering that not all users of the GlobalLab platform took measures to create a project, ideas, work with questionnaires and educational materials, the total number of students for whom the value of all 4 variables differs from the neutral one was 9.7 thousand.
Keywords: Education, learning trajectory, student, educational process, mathematical model