Design and Evaluate a Blended Learning Model for Technical Skills Development

Thesis Abstract

The rapid evolution of technology-driven industries necessitates the development of innovative instructional models to enhance technical skills acquisition among learners. Traditional training approaches often fall short in addressing the diverse learning needs and the urgency for practical competencies critical for workforce readiness. This study aims to design, implement, and evaluate a comprehensive blended learning model specifically tailored for technical skills development in vocational and technical education settings. The primary objective is to determine the effectiveness of the proposed model in improving learners' technical competencies, engagement, and retention compared to conventional training methods. Employing a quasi-experimental research design, the study involved a sample size of 150 students enrolled in a technical training institute, selected through stratified random sampling to ensure representation of different skill levels and backgrounds. Data collection was conducted using a structured skills assessment instrument, learner engagement questionnaires, and focus group discussions. Validity and reliability of the instruments were established through expert review and Cronbach’s alpha coefficients exceeding 0.80. The quantitative data were analyzed using descriptive statistics, paired t-tests, and multiple regression analysis to evaluate the impact of the blended learning intervention on technical skills acquisition and learner engagement. The qualitative data from focus groups were analyzed thematically to interpret learners’ perceptions and experiences. The study hypothesizes that the blended learning model will significantly enhance technical skills performance, learner engagement, and motivation over traditional face-to-face training. It is expected that findings will demonstrate statistically significant improvements in pre- and post-test scores on practical skills assessments (p < 0.01), as well as positive correlations between engagement levels and skill acquisition. The thematic analysis is anticipated to reveal key factors influencing learner motivation and perceptions of the blended approach, including the flexibility of online modules, interactive simulations, and real-time instructor feedback. Theoretically, the study is anchored in the Technological Pedagogical Content Knowledge (TPACK) framework, which informs the integration of technology and pedagogy tailored to technical education. It also draws on the Kirkpatrick Model of training evaluation to assess training effectiveness at multiple levels, including learners' reactions, learning outcomes, behavioral changes, and organizational impact. The research seeks to address the gaps identified in prior studies, such as limited empirical evidence on scalable blended models for technical skills and the need for context-specific evaluations. The results are expected to contribute to existing theoretical understandings of blended learning efficacy within technical education by providing a validated model that integrates best practices in instructional design and technology use. The study will offer practical guidelines for educators and policymakers on implementing blended approaches that align with industry requirements and improve competency development. It will emphasize the importance of student-centered, flexible learning environments that foster active participation and real-world skill application. In conclusion, the findings are anticipated to confirm that well-structured blended learning models can substantially enhance technical skills development, especially in resource-constrained settings where access to conventional training facilities is limited. Based on the evidence generated, the study recommends adopting modular, technology-enhanced instructional strategies, investing in faculty development for effective online pedagogy, and establishing continuous assessment mechanisms to monitor learner progress. Future research directions include exploring longitudinal impacts of blended models on employment outcomes and integrating emerging technologies such as virtual reality to further enrich technical training experiences.