The Knowledge Repository Management System Architecture of Digital Knowledge Engineering using Machine Learning to Promote Software Engineering Competencies

Nattaphol Thanachawengsakul, Panita Wannapiroon, Prachyanun Nilsook


The knowledge repository management system architecture of digital knowledge engineering using machine learning (KRMS-SWE) to promote software engineering competencies is comprised of four parts, as follows: 1) device service, 2) application service, 3) module service of the KRMS-SWE and 4) machine learning service and storage unit. The knowledge creation, storage, testing and assessing of students’ knowledge in software engineering is carried out using a knowledge verification process with machine learning and divided into six steps, as follows: pre-processing, filtration, stemming, indexing, data mining and interpretation and evaluation. The overall result regarding the suitability of the KRMS-SWE is
assessed by five experts who have high levels of experience in related fields. The findings reveal that this research approach can be applied to the future development of the KRMS-SWE.


System Architecture, KRMS-SWE, Digital Knowledge Engineering, Machine Learning.

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Copyright (c) 2019 Nattaphol Thanachawengsakul, Panita Wannapiroon, Prachyanun Nilsook

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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