QUANTITATIVE LARGE-SCALE STUDY OF SCHOOL STUDENT'S ACADEMIC PERFORMANCE PECULIARITIES DURING DISTANCE EDUCATION CAUSED BY COVID-19

Abstract

The paper presents the large-scale analysis results of the distance learning impact caused by COVID-19 and its influence on school student's academic performance. This multidisciplinary study is based on the large amount of the raw data containing school student's grades from 2015 till 2021 academic years taken from "Electronic education in Tatarstan Republic" system. The analysis is based on application of BigData and mathematical statistics methods, realized by using Python programming language. Dask framework for parallel cluster-based computation, Pandas library for data manipulation and large-scale analysis data is used. One of the main priorities of this paper is to identify the impact of different educational system's factors on school student's academic performance. For that purpose, the quantile regression method was used. This method is widely used for processing a large-scale data of various experiments in modern data science. Quantile regression models are designed to determine conditional quantile functions. Therefore, this method is especially suitable to exam conditional effects at various locations of the outcome distribution: e.g., lower and upper tails. The study-related conditional factors include such factors as student's marks from previous academic years, types of lessons in which grades were obtained, and various teacher's parameters such as age, gender and qualification category.