ANALYSIS OF STUDENT PERFORMANCE BASED ON CANONICAL CORRELATION ANALYSIS

Abstract

The article examines the application of Canonical Correlation Analysis (CCA) to investigate the relationships between student performance outcomes across different groups of disciplines. The disciplines were categorized into the following groups: mathematics, programming and algorithms, systems design, networks and distributed systems, applied software and technologies, and economic and managerial disciplines. The study aims to identify dependencies between these discipline groups that influence overall academic performance.

The analysis revealed that discrete mathematics plays a key role in shaping programming skills, with performance in mathematical disciplines significantly correlating with outcomes in other fields. Both strong and weak correlations were identified between specific discipline groups. The use of CCA provided deeper insights into the relationships between subjects, offering new opportunities for optimizing the educational process.

The findings of the article have both theoretical and practical significance, contributing to the improvement of educational approaches and methods for assessing academic performance.