SHAPLEY VALUES TO EXPLAIN MACHINE LEARNING MODELS OF SCHOOL STUDENT'S ACADEMIC PERFORMANCE DURING COVID-19

ABSTRACT

In this work we perform an analysis of distance learning format influence, caused by COVID-19 pandemic on school students' academic performance. This study is based on a large dataset consisting of school students grades for 2020 academic year taken from "Electronic education in Tatarstan Republic" system. The analysis is based on the use of machine learning methods and feature importance technique realized by using Python programming language. One of the priorities of this work is to identify the academic factors causing the most sensitive impact on school students' performance. In this work we used the Shapley values method for solving this task. This method is widely used for the feature importance estimation task and can evaluate impact of every studied feature on the output of machine learning models. The study-related conditional factors include characteristics of teachers, types and kinds of educational organization, area of their location and subjects for which marks were obtained.