Decision Making Methodology to Assess Big Data Professional Education in Vocational and Technical Colleges: Forest HyperSoft Set Approach and Implementation

Abstract

The rapid advancement of big data technologies has transformed industries, 
necessitating the development of specialized educational programs to equip students with 
relevant skills. Vocational and technical colleges play a crucial role in bridging the skill gap by 
offering big data professional education tailored to industry demands. However, assessing the 
quality and effectiveness of such programs remains a challenge due to the evolving nature of big 
data, the need for practical training, and alignment with industry requirements. This study 
proposes a comprehensive evaluation framework incorporating multiple criteria such as 
curriculum relevance, faculty expertise, infrastructure, industry collaboration, and student 
outcomes. By employing a Multi-Criteria Decision-Making (MCDM) approach, this research 
provides an in-depth analysis of big data education quality, ensuring that vocational institutions 
produce industry-ready graduates. We use two MCDM methods such as CRITIC method to 
compute the criteria weights and the VIKOR method to rank the alternatives. These methods are 
used with the Forest HyperSoft set to deal with criteria, sub criteria and sub-sub-criteria. We use 
five criteria and six alternatives in this study. These criteria are divided into Trees. Then we 
compute the criteria weights and rank the alternatives under each criterion. Then we obtain the 
rank of each criterion and combine these ranks into a final rank. 

DOI: 10.5281/zenodo.15036671