Intelligent Assessment of Sustainable Engineering Employability Skills for Quality Education and Decent Work Using Multilayer Perceptron Neural Networks

Abstract

The evaluation of engineering students’ employability competencies is essential for bridging the gap between academic preparation and industry requirements while supporting sustainable workforce development. Identifying these competencies contributes to the development of an Engineering Employability Skills framework that assists employers in assessing the relevance of graduate capabilities and enables students to recognize strengths, address skill gaps, and enhance their professional readiness. This study investigates the level of consensus among industry professionals and academicians regarding the employability skills considered critical for preparing engineering graduates for an evolving and sustainable labor market. Artificial Neural Networks (ANNs) were employed to analyze complex categorical data, demonstrating their effectiveness in educational research and quantitative modeling. Specifically, Multilayer Perceptron (MLP) neural networks were applied to generate predictive insights and evaluate students’ employability from the employer’s perspective. A Back-Propagation Neural Network (BPN) model was developed to process employer input and classify organizations according to background characteristics and other factors influencing graduate employability outcomes. Through this intelligent and data-driven assessment approach, the study offers a novel methodology for evaluating engineering employability skills and generating actionable insights for higher education institutions, policymakers, and industry stakeholders. The findings contribute to the advancement of sustainable engineering education and talent development by supporting the achievement of the United Nations Sustainable Development Goals, particularly SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth), through the promotion of industry-relevant competencies, lifelong learning, and workforce preparedness.