A Competency-Based Framework for Technical Education Pathways

Thesis Abstract

This study investigates a Competency-Based Framework for Technical Education Pathways to align learning outcomes with labor-market demands and to enhance graduate employability across technical disciplines. The problem addressed is the misalignment between traditional time-based curricula in technical education and the evolving competencies required by industry partners, which contributes to skill gaps, extended degree durations, and underemployment among graduates. The aim is to develop, validate, and propose a scalable competency-based framework that integrates defining competencies, outcome-based assessment, industry collaboration, and flexible progression pathways. Specific objectives include (1) identifying core competencies required by major technical sectors (manufacturing, information technology, and renewable energy) through a mixed-methods synthesis of employer surveys and expert interviews; (2) constructing a competency framework with defined proficiency levels, assessment rubrics, and progression criteria; (3) validating the framework with stakeholders using a Delphi panel (n=25) and a pilot implementation in three technical schools; (4) evaluating the impact of the framework on student learning outcomes, time-to-competence, and employability indicators; and (5) prototyping a scalable implementation model for policy and curriculum reform. The methodology adopts a sequential exploratory mixed-methods design grounded in social constructivism and competency theory. A comprehensive literature scan identifies theoretical underpinnings from Bloom’s revised taxonomy and Dreyfus and Dreyfus skill acquisition theory, complemented by contemporary competency-based education (CBE) models such as the Guadalupe framework and work-integrated learning principles. The population includes technical education faculty, industry partners, and undergraduate and diploma students across three tertiary institutions. The study employs a multi-stage sampling strategy purposive sampling of industry partners (n=40) for needs assessment, stratified sampling of faculty (n=60) for framework development, and random sampling of students (n=240) for pilot testing. Data collection uses (i) semi-structured interviews and focus groups with industry leaders and faculty (conducted in two rounds, totaling ~40 interviews), (ii) an online survey of 200 students to gauge baseline competencies and employability perceptions, and (iii) document analysis of existing curricula, assessment rubrics, and industry standards. Instrument validity is supported by expert review, and reliability is established via Cronbach’s alpha (target ? ? 0.80) for the student and faculty surveys. For data analysis, thematic analysis is applied to qualitative data using NVivo, while quantitative data are analyzed with descriptive statistics, confirmatory factor analysis (CFA) to validate the competency structure, and structural equation modeling (SEM) to examine relationships among competencies, learning outcomes, and employability indicators. A pilot test with 120 students over one academic year evaluates time-to-competence and progression rates before and after framework adoption. The model specification includes latent variables for Core Technical Competencies, Transferable Skills, Assessment Fidelity, and Industry Alignment, with hypothesized paths to Academic Performance, Time-to-Competence, and Employment Readiness. Key expected findings include identification of a compact set of competency clusters that reliably predict successful entry into technical roles, evidence that structured progression and performance-based assessment accelerate time-to-competence by 20–30% relative to traditional curricula, and demonstration of strong associations between industry-aligned rubrics and improved employability metrics such as internship uptake, job placement rates, and starting salaries. The study anticipates differential effects by discipline, with manufacturing and information technology showing more pronounced gains due to rapid technology cycles, while renewable energy requires longer apprenticeship-type pathways. The contribution to knowledge lies in producing a rigorously validated, scalable competency framework for technical education that integrates pedagogy, assessment, and industry partnerships within a coherent pathway model, thus extending competency-based education theory to diverse technical fields and providing a replicable blueprint for policy formulation and curriculum reform. The study also offers a practical model for governance, resource planning, and professional development needs to sustain the framework beyond pilot institutions. The main conclusion posits that a well-structured Competency-Based Framework for Technical Education Pathways can harmonize curriculum design with industry expectations, shorten time-to-competence, and enhance graduate employability without compromising disciplinary rigor. Recommendations include adopting industry-informed competency catalogs with tiered proficiency standards, implementing standardized rubrics and performance-based assessments across programs, strengthening work-integrated learning through structured placements, and scaling the framework through national-level policy alignment, funding incentives, and continuous feedback loops between educators and employers. Suggestions for further research emphasize longitudinal tracking of graduates to assess long-term career progression and the framework’s applicability across additional technical domains and geographic contexts.