Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2904-2907 | Volume-9 Issue-3, February 2020. | Retrieval Number: C4830029320/2020©BEIESP | DOI: 10.35940/ijeat.C4830.029320
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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 model is based on the use of interactive teaching methods. A characteristic feature of the use of interactive technologies is the organization of training that takes into account the inclusion of all the students of a group without exception in the learning process. Joint activity means that each participant makes his or her own individual contribution, whereby in the course of work there is an exchange of knowledge, ideas, and methods of activity. An environment of educational communication is created that is characterized by openness, interaction of participants, equality of their arguments, accumulation of common knowledge, and the possibility of mutual evaluation and control. The use of neural networks to study and predict educational assets will provide research and development organizations and teams with innovative and effective ways of conducting research in the field of educational theory, modeling of the cognitive processes related to formation of different student competencies, and devising more appropriate methods for estimating student educational outcomes.
Keywords: Mathematical model, machine learning, GlobalLab, educational trajectory, Jupyter Notebook, GNU Octave s