Beyond Code Correctness: A Hybrid Behavioral-Cognitive Model for Student Assessment in Programming Education

Abstract

 Traditional programming assignment assessments primarily focus on evaluating the correctness of the final output, often neglecting critical aspects such as engagement, collaboration, and debugging skills. As programming education increasingly shifts toward project-based and collaborative learning, there is a growing need for more holistic evaluation methods. This paper proposes a hybrid conceptual framework that integrates cognitive correctness metrics with behavioral analytics derived from Source Code Management (SCM) systems, such as GitHub. The framework captures authentic student behaviors during assignment development and leverages machine-learning models combined with explainable AI techniques to transparently predict grades. This conceptual study advocates a paradigm shift from purely product-based assessments to a more process-oriented, learning-centered evaluation model that promotes deeper and more meaningful educational experiences in programming education.