Advanced Course Equivalence Certification Using NLP Technology through Word Mover's Distance in Global Academic Credit Transfers

As the competition for higher education and professional employment intensifies globally, the proper evaluation and translation of academic credits and qualification systems has taken on a key significance. Most classic techniques for evaluating the contents of equivalence course certificates, such as Jaccard Index or Cosine, are too simplistic and do not reveal differences in educational credentials or course materials that are found at a semantically deeper structure. These techniques are primarily based on the surface-level characteristics of the texts, ignoring their inherent pictures. To overcome these challenges, this paper considers the possibility of using methods of Natural Language Processing (NLP), especially Word Mover's Distance, in the context of higher education and professional qualification, academic credit and course equivalency evaluation. WMD, or Word Mover's Distance, is a more sophisticated method of NLP, which describes the food transport distance problem and searches for the optimal distance between two different documents. The advantages of this methodology lie in comparing the course description and qualifications from a different perspective without losing the true meaning of these words in relation to course equivalence certificate comparison. On the basis of our study, we can state that WMD implements better than other conventional approaches and is an appealing solution for credit system transfer, the degree equivalence process evaluation in higher education, and employment clearance services. In this way, WMD applications for NLP purposes will allow for the sustainable recognition of the international qualifications of professionals and students, thus enhancing their movement across borders.