Аннотации:
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.
