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

Nattaphol Thanachawengsakul, Panita Wannapiroon, Prachyanun Nilsook

Abstract


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.

Keywords


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

Full Text:

PDF


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


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
Creative Commons License
Indexing:
Scopus logo Clarivate Analyatics ESCI logo EI Compendex logo IET Inspec logo DOAJ logo DBLP logo Learntechlib logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo