We used eight different machine learning methods to solve the regression task of forecasting value of
Cohen’s effect size . We determined the values of the error function corresponding to all applied
algorithms and established school classes for which prediction is easier and the ones for which
prediction is more difficult.
We discovered impact of age of teachers to the forecasting of parameter; lessons for which marks were
more significant in the studied task and areas of Tatarstan Republic, location of school in which
increased or decreased Cohen’s effect size. Moreover, we discovered that the kind of educational
organization also plays a special role in the forecasting task and identified the ones which had a
significant impact on the value of Cohen’s effect size. The impact of these study-related factors may
indicate that different schools, school types and teacher had different periods of adaptation to a rapidly
changing learning format, and these changes can be evaluated using feature importance method in
combination with machine learning algorithms.
The results obtained during the research, after appropriate verification, may be used to evaluate the
influence on academic performance of school students after introduction of distance learning.
ACKNOWLEDGMENTS
The study (all theoretical and empirical tasks of the research presented in this paper) was supported by
a grant from the Russian Science Foundation, project № 22-28-00923, “Digital model for predicting
the academic performance of school-children during school closings based on big data and neural
networks”.
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https://doi.org/10.17993/3ctic.2022.112.136-144
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