Knowledge engineering management system on cloud technology for externship students

2016 ◽  
Author(s):  
Anuchit Anupan ◽  
Prachyanun Nilsook ◽  
Panita Wannapiroon
Keyword(s):  
Management System ◽  
Knowledge Engineering ◽  
Engineering Management ◽  
Cloud Technology

Facilities Engineering Management System Study: A Review of Functional Areas in Selected Army Installation Directorates of Engineering and Housing

1989 ◽  
Author(s):  
Sine L. Hill ◽  
Sandra Kappes ◽  
David M. Bailey ◽  
Michael J. Binder ◽  
Gerald J. Brown ◽  
...  
Keyword(s):  
Management System ◽  
Engineering Management ◽  
System Study ◽  
Functional Areas

Metrics for Concept-Oriented Knowledge Bases

1998 ◽  
Vol 08 (02) ◽  
pp. 161-188 ◽  
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.

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

2019 ◽  
Vol 14 (12) ◽  
pp. 42 ◽  
Author(s):  
Nattaphol Thanachawengsakul ◽  
Panita Wannapiroon ◽  
Prachyanun Nilsook
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
System Architecture ◽  
Management System ◽  
Knowledge Engineering ◽  
Research Approach ◽  
Storage Unit ◽  
Knowledge Repository ◽  
Application Service ◽  
Digital Knowledge

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.

scholarly journals Student Relation Management on Cloud Technology for Academic and Internship Counseling Model in Rajabhat University

2019 ◽  
Vol 9 (4) ◽  
pp. 148
Author(s):  
Phatthranit Srisakonsub ◽  
Namon Jeerungsuwan ◽  
Pallop Piriyasurawong
Keyword(s):  
Management System ◽  
Demographic Data ◽  
Design Stage ◽  
Model Assessment ◽  
Cloud Technology ◽  
Academic Counseling ◽  
Teacher Students ◽  
Main Components ◽  
Counseling Model ◽  
The University

The purposes of this study were: 1) to design a model of student relation management system on cloud technology for academic and internship counseling in Rajabhat University and 2) to assess a model of student relation management system on cloud technology for academic and internship counseling in Rajabhat University. The research methods were of 2 stages: 1) the model design stage in which 1.1) documents, textbooks and related literature were reviewed and analyzed, and 1.2) the model was designed, using elements and guidelines obtained from those documentaries together with the Front-end Analysis of needs, learners, environment and technology; and 2) the model assessment stage which included 5 steps, namely, 2.1) constructing instruments for model assessment, 2.2) having 9 experts to assess the instruments, 2.3) analyzing the data obtained from the assessment, using mathematic mean (x̄) and standard deviation (S.D.), 2.4) improving the model according to the experts ‘advises, and 2.5) presenting the model. It was found that the designed model was made up of 3 main components: 1) student relation management which consisted of 1.1) data base, 1.2) cloud technology, 1.3) creating relation, and 1.4) maintaining relation; 2) student relation activities of the management system on cloud technology that comprised 2.1) filtering teacher students’ demographic data, 2.2) connecting the data from the university with the university system, 2.3) publicizing information and news, 2.4) formulating academic counseling plan, 2.5) carrying out academic counseling, and 2.6) following up and evaluating; and 3) academic and internship counseling which was composed of 3.1) registration, 3.2) orientation, 3.3) supervision planning, 3.4) supervising, 3.5) academic counseling, following up the academic counseling, and 3.6) evaluation. The study revealed that the developed model was very appropriate to be used for academic and internship counseling.

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