International Journal of
Information and Education Technology

Abstract

Evaluations of program educational objectives, graduation requirements, and curriculum systems in engineering education certification are often confounded by multiple interfering factors, limiting their accuracy. Traditional experience-driven approaches, such as simplistic scoring or quantile-based methods, frequently fail to isolate these confounding influences, leading to biased assessments. To address this critical limitation, we propose a novel two-phase framework: Mediator-based Deconfounding and Continuous Improvement (M-DCI). Building upon causal inference principles, specifically leveraging the front-door criterion for path analysis, M-DCI isolates and eliminates confounding factors to accurately assess the true attainment of course objectives and subsequently drives targeted, evidence-based improvements. Using the “Information Security” course in a Communication Engineering program as a case study, we constructed a causal model and applied the M-DCI framework. This study establishes a robust, closed-loop “evaluation-feedback-improvement” mechanism, demonstrating a significant shift from experience-driven to evidence-driven practices. The M-DCI framework provides a precise and actionable methodology for continuous quality enhancement in engineering education certification.