Designing an SKKNI-Aligned Learning Ecosystem for Employability: A Competency-Clustering Framework for MICE Certification

Abstract

MICE certification requires a learning ecosystem that connects training providers, professional certification bodies, assessors, industry, professional communities, digital technology, and project experience. The initial proposal treated learning ecosystems, employability, and competency clustering as equivalent causal variables, creating a conceptual inconsistency because clustering is an analytical technique rather than a latent construct. This conceptual article develops a more coherent framework in which the learning ecosystem predicts employability directly and indirectly through MICE competency attainment, while clustering is used to map competency profiles. The study combines theory synthesis with directed analysis of 114 competency units in Indonesian Minister of Manpower Decision No. 123/2024, publicly available BNSP certification schemes, and literature on work-based learning, employability, scale development, PLS-SEM, and cluster validation. The resulting framework specifies five learning-ecosystem dimensions, seven function-based competency macro-domains, four testable hypotheses, and a four-stage validation protocol. Empirical validation is planned with at least 400 active MICE certification participants or graduates using content validation, psychometric assessment, PLS-SEM, and domain-score clustering. The framework contributes by distinguishing learning processes, competency attainment, employability, and clustering outputs, thereby providing a basis for testing, replication, and targeted learning pathways. No empirical effects are claimed before participant-level data are collected.