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

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.

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PRACTICAL MACHINE LEARNING FOR SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING

Handbook of Software Engineering and Knowledge Engineering ◽

10.1142/9789812389718_0035 ◽

2001 ◽

pp. 837-862 ◽

Cited By ~ 11

Author(s):

TIM MENZIES

Keyword(s):

Machine Learning ◽

Software Engineering ◽

Knowledge Engineering

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The Development of a MOOCs Knowledge Repository System Using a Digital Knowledge Engineering Process to Enhance Digital Entrepreneurs’ Competencies

International Journal of Engineering Pedagogy (iJEP) ◽

10.3991/ijep.v11i6.24409 ◽

2021 ◽

Vol 11 (6) ◽

pp. 85-101

Author(s):

Nattaphol Thanachawengsakul ◽

Panita Wannapiroon

Keyword(s):

Human Performance ◽

Knowledge Engineering ◽

Small And Medium Enterprises ◽

Competency Assessment ◽

Engineering Process ◽

Significance Level ◽

Knowledge Repository ◽

Digital Knowledge ◽

Dependent Sample ◽

Medium Enterprises

The objectives of this research were as follows: the development of a MOOCs knowledge repository system using a digital knowledge engineering process, and a competency assessment of digital entrepreneurs engaging in a MOOCs knowledge repository system using a digital knowledge engineering process. A total of 30 people were selected as a sampling group (purposive sampling) for this study, these included Small and Medium Enterprises (SMEs) in Bangkok with expertise in Human Performance Technology (HPT), as well as the MOOCs knowledge repository system. The mean, standard deviation, percentage, and a dependent sample t-test were used in the procedure for data analysis. The research findings suggested that: (1) the overall result concerning the development of a MOOCs knowledge repository system using a digital knowledge engineering process was at the highest level (Mean = 4.89, S.D. = 0.31), and (2) the overall result regarding the competencies of digital entrepreneurs after engaging in a MOOCs knowledge repository system using a digital knowledge engineering process passed the 80% rating, according to criteria. Moreover, learners who had undertaken activities through a MOOCs knowledge repository system using a digital knowledge engineering process improved their learning outcomes with a significance level of .05 based on the research hypothesis.

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Metrics for Concept-Oriented Knowledge Bases

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s021819409800011x ◽

1998 ◽

Vol 08 (02) ◽

pp. 161-188 ◽

Cited By ~ 9

Author(s):

Timothy C. Lethbridge

Keyword(s):

Knowledge Management ◽

Software Engineering ◽

Knowledge Base ◽

Management System ◽

Software Metrics ◽

Knowledge Engineering ◽

Knowledge Bases ◽

Knowledge Management System ◽

The Other ◽

Develop Software

Metrics are widely researched and used in software engineering; however there is little analogous work in the field of knowledge engineering. In other words, there are no widely-known metrics that the developers of knowledge bases can use to monitor and improve their work. In this paper we adapt the GQM (Goals-Questions-Metrics) methodology that is used to select and develop software metrics. We use the methodology to develop a series of metrics that measure the size and complexity of concept-oriented knowledge bases. Two of the metrics measure raw size; seven measure various aspects of complexity on scales of 0 to 1, and are shown to be largely independent of each other. The remaining three are compound metrics that combine aspects of the other nine in an attempt to measure the overall 'difficulty' or 'complexity' of a knowledge base. The metrics have been implemented and tested in the context of a knowledge management system called CODE4.

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Synthesis of Digital Knowledge Engineering Repository Management System

International Journal of e-Education e-Business e-Management and e-Learning ◽

10.17706/ijeeee.2019.9.4.348-356 ◽

2019 ◽

Vol 9 (4) ◽

pp. 348-356

Author(s):

Nattaphol Thanachawengsakul ◽

◽

Panita Wannapiroon ◽

Prachyanun Nilsook

Keyword(s):

Management System ◽

Knowledge Engineering ◽

Digital Knowledge

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Development of a Learning Ecosystem Using Digital Knowledge Engineering Through MOOCs Knowledge Repository System

International Journal of Engineering Pedagogy (iJEP) ◽

10.3991/ijep.v11i1.15011 ◽

2021 ◽

Vol 11 (1) ◽

pp. 35

Author(s):

Nattaphol Thanachawengsakul ◽

Panita Wannapiroon

Keyword(s):

Learning Outcomes ◽

Learning Process ◽

Knowledge Engineering ◽

Learning Activities ◽

Instructional Tools ◽

Knowledge Repository ◽

Learning Ecosystem ◽

Digital Knowledge ◽

Further Development

This paper presents the development of a learning ecosystem using digital knowledge engineering through a MOOCs knowledge repository system. This system comprises four parts: (1) Stakeholders, including instructors and learners; (2) A digital knowledge engineering learning process that describes the roles of instructors and learners, learning activities, and instructional tools; (3) A MOOCs knowledge repository system, which is the software de-veloped to enhance digital entrepreneurs’ competencies; and (4) Digital entrepreneurs’ competencies, which describe learning outcomes using the digital knowledge engineering learning process. The suitability of the learning ecosystem was assessed by twelve experts possessing at least three years of related experience. They judged its suitability to be at the highest level. This study can be used to support further development of the MOOCs knowledge repository system assess digital entrepreneurs’ competencies according to digital knowledge engineering learning process.

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