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

Authors: Dongfang Li; Xiaohua Wei; Hua Zhang; Huiyi Zhu; Qianhua Du

Addresses: Mechanical Engineering, Quzhou College of Technology, Quzhou, 324000, China ' Mechanical Engineering, Quzhou College of Technology, Quzhou, 324000, China ' Mechanical Engineering, Quzhou College of Technology, Quzhou, 324000, China ' Mechanical Engineering, Quzhou College of Technology, Quzhou, 324000, China ' Mechanical Engineering, Quzhou College of Technology, Quzhou, 324000, China

Abstract: 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.

Keywords: deep learning; outcome-based education; OBE; vocational training; ensemble strategies; educational policies.

DOI: 10.1504/IJICT.2025.146697

International Journal of Information and Communication Technology, 2025 Vol.26 No.19, pp.18 - 33

Received: 17 Mar 2025
Accepted: 16 Apr 2025
Published online: 13 Jun 2025 *