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

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.