Journal of Siberian Federal University. Humanities & Social Sciences / Collective Intelligence Algorithms in Pedagogical Practice

Full text (.pdf)
Issue
Journal of Siberian Federal University. Humanities & Social Sciences. 2021 14 (3)
Authors
Koliada, Mykhailo G.; Bugayova, Tatyana I.
Contact information
Koliada, Mykhailo G.: Donetsk National University Donetsk, Ukraine; ; ORCID: 0000-0001-6206-4526; Bugayova, Tatyana I.: Donetsk National University Donetsk, Ukraine: ; ORCID: 0000-0003-1926-1633
Keywords
collective mind; collective intelligence; ant colony algorithm; self-organization; selection of teaching method
Abstract

The article considers the ideas of using artificial intelligence algorithms in pedagogics. It presents the methodology of the so-called collective pedagogical megasystem. The introduction of such an ephemeral construct is necessary only to understand the collective pedagogical intelligence system, formulate it in a model, find out its operation patterns and the laws it obeys. It would contribute to predicting pedagogical processes and phenomena and formulating new laws. The objective of the article is to demonstrate the application of collective intelligence algorithms in pedagogical practice for effective didactic decision-making. The matter is that in a real educational process, besides the well-known set of pedagogical conditions, there are some random and unpredictable reasons and factors that are hard to foresee or anticipate. Due to their stochastic nature, they occur spontaneously. These single reasons make a minor impact on the teaching methods selection, but in aggregate their influence gets so strong that they can upturn some prognostic conclusions. The problem also focuses on identifying the factors that would ensure the highest efficiency and productivity of studies among the known (expected) and random (unexpected) reasons. For these purposes, the most suitable algorithm for the selection training methods is the so-called ant algorithm which, on the one hand, considers the randomness of the influence parameters, and on the other, ensures steady and high productivity. A certain example was selected to demonstrate the process of applying the ant algorithm to reveal the best hierarchy of the pedagogical conditions (factors) that determines the optimum choice of the training method. The authors conclude that human intelligence is distributed and integrated at the same time, and the application of collective intelligence algorithms in pedagogical practice can yield some effective didactic decisions

Pages
327–340
DOI
10.17516/1997–1370–0724
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/139951

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).