Abstract
This study addresses the improvement of diagnostic and analytical assessment methodologies for student programming skills within a digital learning environment. It analyzes the limitations of traditional, correctness-only automated systems and proposes an integrated approach that evaluates the student’s reasoning process, debugging strategies, error patterns, and algorithmic thinking. The methodology leverages code tracing, mutation testing, algorithm simulation, cognitive diagnostic models, and artificial intelligence. Furthermore, the paper outlines the architecture and pedagogical role of the CodeLearn Pro digital platform within the framework of doctoral research.
References
This work is licensed under a Creative Commons Attribution 4.0 International License.