Manuscript received May 22, 2023; revised July 3, 2023; accepted September 12, 2023.
Abstract—This study delves into the emerging opportunities
and challenges arising from the integration of education and
artificial intelligence in the unique backdrop of the COVID-19
pandemic. Its primary objective is to develop an optimized
ensemble model that sheds light on the surge in learning
engagement among secondary school students during
Emergency Distance Learning (EDL) amid the pandemic. To
achieve this, we explored three distinct methodologies: the
k-Nearest Neighbor method (KNN), Random Forest (RF), and
Gradient Boosting (XGB). Our approach involved constructing
an ensemble model that synthesized the strengths and
weaknesses of these individual models based on their training
outcomes. In contrast to prevailing beliefs that Emergency
Distance Learning (EDL) negatively impacts education, our
study's findings underscore a positive upswing in students'
learning activity during EDL. Furthermore, our ensemble
model effectively identifies the underlying reasons behind this
increased engagement, achieving an impressive overall
accuracy rate of 87% in processing the survey responses. Our
research encompassed a comprehensive sample, targeting
35,950 secondary school students from 16 regions and cities of
significant importance within Kazakhstan. This diverse sample
included students from urban, rural, and small schools,
providing a well-rounded perspective on territorial affiliation.
Data collection was conducted through an online survey using a
methodologically verified structured questionnaire.
Index Terms—COVID data, KNN, Random Forest, XGBoost,
emergency distance learning, learning activity
Manargul Mukasheva is with National Academy of Education named
after Y. Altynsarin, Astana, Kazakhstan.
Ainur Mukhiyadin, Ulzhan Makhazhanova, and Sandugash Serikbayeva
are with Faculty of Information Technology, L.N. Gumilyov, Eurasian
National University, Astana, Kazakhstan.
*Correspondence: amukhiyadin@gmail.com (A.M.)
Cite: Manargul Mukasheva, Ainur Mukhiyadin*, Ulzhan Makhazhanova, and Sandugash Serikbayeva, "The Behaviour of the Ensemble Learning Model in Analysing Educational Data on COVID-19," International Journal of Information and Education Technology vol. 13, no. 12, pp. 1868-1878, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).