Scalable Computing-Driven Innovation in Vocational Education using Machine Learning and Big Data

Abstract

Education institutions utilize scalable computing to handle ever-increasing volumes and varieties of data effectively. The study shows how scalable computing makes it easier to manage resources and helps change learning environments in real-time, leading to better and more efficient education. This research utilizes scalable computational tools to investigate how business education embeds machine learning and big data analysis within its innovation strategy and practices. Their performance on an educational level determines one’s ability to contribute to economic and social development. Traditional business education faces issues such as cultivating experiences, accurately measuring student achievement and adapting quickly to new market expectations. Hence, this study proposes the statistical learning analysis for policy data analysis (SLA-PDA), which uses machine learning techniques, big data analytics, and scalable computing to analyze educational data, find trends, predict, and develop personalized learning strategies. The framework enhances decision-making capacities in educational practice by making decisions based on insights from data. Simulation: An exhaustive examination of the outcomes confirms the validity of the proposed methodology and indicates that vocational education programs can be made more standardized and effective overall. Considerations highlight how the SLA-PDA can be used in various educational settings, such as curriculum design, student performance evaluation, and resource allocation. Findings from this study indicate that employee training should be enhanced using advanced data analytics and machine learning. The proposed SLA-PDA methodology achieves a 96.3% accuracy in analyzing student performance, 96.8% in improving student progress assessment, 97.52% in resource allocation, 98.15% in integration for decision-making, and 98.16% in scalable computing within the virtualized school context.