IMPROVING THE METHODOLOGY FOR DIAGNOSTIC ASSESSMENT OF PROGRAMMING SKILLS IN A DIGITAL LEARNING ENVIRONMENT: THE CASE OF THE CODELEARN PRO PLATFORM
Keywords:
Digital learning environment, programming competency, diagnostic assessment, automated analysis, mutation testing, debugging strategies, cognitive diagnostics, algorithm simulation, formative assessment, artificial intelligence, CodeLearn Pro.Abstract
This article addresses issues of improving the methodology for diagnostic and analytical assessment of students' programming skills in a digital learning environment. The study analyzes the limitations of traditional automated assessment systems and proposes an integrated methodology aimed at evaluating not only the final code output, but also the student's reasoning process, debugging strategies, error patterns, and algorithmic thinking. The methodology is grounded in code tracing, mutation testing, algorithm simulation, pedagogical visualization, cognitive diagnostic models, and artificial intelligence technologies. The article further examines the architecture, functional capabilities, and methodological role of the CodeLearn Pro digital learning platform within the context of doctoral research.
