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September 10, 2025

Development of a methodology for evaluating educators using automation tools

Key Points

The study creates a methodology for automated evaluation of teacher performance utilizing digital data.
Automated evaluation incorporates factors such as student test results and engagement metrics from 120 teachers.
Methods employed include machine learning techniques and statistical analysis to process multi-format data.
Findings may enhance the quality of education, suggesting improvements over traditional subjective evaluation methods.

Abstract

Under current conditions of improving the quality of education, an objective and timely assessment of teachers’ performance is highly relevant. Traditional expert methods based on surveys and observations are often subjective, labor-intensive, and do not provide prompt feedback. The aim of this study is to develop and validate a methodology for automated evaluation of teaching activities based on digital data obtained in the educational environment. The methodology includes the collection of multi-format data: student test results, log files from electronic educational platforms, student engagement data (login frequency, activity in discussions), and expert assessments. For analysis, machine learning methods and statistical analysis were used. The sample comprised data from 15 schools and 120 teachers over the course of an academic year.

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