Deep learning ensemble strategies in outcome-based education: the influence of ideology and politics on vocational training

Outcome-based education (OBE) has emerged as a transformative model for aligning learning outcomes with vocational training objectives. This paper explores the intersection of deep learning ensemble strategies with ideological and political dimensions within OBE frameworks. A novel ensemble learning model is proposed to predict and assess educational outcomes across varied socio-political contexts. The study emphasises how political influences shape curriculum design, resource distribution, and vocational standards. Results indicate that deep learning ensembles offer scalable and effective tools for mapping policy-driven variables to educational success. By bridging technology and policy, this research provides a framework for stakeholders to enhance OBE systems in diverse political settings, ultimately promoting fairness and adaptability in vocational education.