A SYSTEMIC CONCEPTUAL FRAMEWORK FOR APPLYING BLOCKCHAIN TO ENHANCE SECURITY AND TRANSPAPRENCY IN ACADEMIC CREDIT TRANSFER
Keywords:
Optical Character Recognition (OCR), Natural Language Processing (NLP), Blockchain, Academic Credit TransferAbstract
Currently, academic record management and credit transfer systems in Thai educational institutions face significant challenges, including processing delays, complex data management, and vulnerability to document forgery, which collectively undermine operational credibility. Therefore, this research aims to develop a systemic conceptual framework for secure and transparent academic record management and credit transfer by integrating Optical Character Recognition (OCR), Natural Language Processing (NLP), and blockchain technology. OCR facilitates the rapid conversion of information from physical documents or images into digital text, while NLP analyzes and structures the extracted data—such as course details, grades, and curriculum content—to support a systematic credit transfer process. Furthermore, blockchain is utilized for secure data storage, leveraging its decentralized architecture and resistance to unauthorized modifications.
The proposed conceptual framework and system architecture were evaluated for appropriateness by 14 experts. The evaluation was divided into three phases: the Preparation Phase, the Core Technology Development Phase, and the Verification and Validation Phase. The average scores obtained for each phase were 4.07, 4.10, and 4.30, respectively, reflecting strong confidence in the accuracy, security, and overall capability of the framework.
The findings indicate that the proposed system architecture can significantly enhance educational data management by improving transparency, strengthening security, and reducing procedural complexity. Moreover, it serves as a valuable applied guideline to effectively support educational institutions in their digital transformation.
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