Identifying Future Workforce Skills Through Industry Partnership Analytics: An Exploratory Framework for Smart Education Systems
Year: 2026
Type: conference-paper
Study design: Observational Study
Abstract: The accelerating pace of workforce transformation puts an imperative on higher education institutions to adopt data-driven approaches in considering emerging skill requirements as a variable for curriculum development and the delivery of outcomes-based education (OBE). Ideally, employer feedback is collected efficiently through strong institutional partnerships and engagement with graduates' employers. However, in practice, such feedback mechanisms remain underutilized, resulting in educational planning processes that are often insufficiently informed by data-driven insights. This study examines employer perceptions of graduate competencies and future workforce skills and proposes an exploratory analytics-enabled framework for smart education systems. Using survey data from the top employers of graduates of Mapúa Malayan Colleges Laguna in the Philippines, employing descriptive statistics and thematic frequency analysis in assessing current skill performance, identify emerging competencies, and prioritize skill development areas. Results indicate strong graduate performance in soft skills, particularly communication and interpersonal competencies, alongside growing industry demand for advanced digital, analytical, and adaptive skills. A Skill Priority Matrix integrates current readiness with future demand signals to identify priority areas for curriculum and credentialing decisions. Employer recommendations for micro-credentials further emphasize the need for flexible learning pathways aligned with workforce trends. The findings demonstrate how structured analysis of industry partnership data can serve as a decision-support mechanism for curriculum development and workforce responsiveness.
Institution: Malayan Colleges Laguna
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7202276150