ВИКОРИСТАННЯ ІНТЕЛЕКТУАЛЬНОГО АНАЛІЗУ В АВТОМАТИЗОВАНИХ СИСТЕМАХ СОРТУВАННЯ

Technology integration in education has grown in a more important role during the COVID-19 epidemic. As standard manual grading techniques have been shown to be insufficient – automation grading provides a more effective approach. However, assessing programming activities is challenging because students employ a variety of coding styles and technical stacks. Manual evaluation offers flexibility but lacks consistency and scalability. Furthermore, advanced levels of programming skill requires completely new approaches to task assessment. To solve these issues, improved approaches including both dynamic and static code analysis, reinforced by machine learning techniques, are essential.