Modelling a semantic knowledge management system for collaborative learning environment using a structural equation modelling

Zaihisma Che Cob, Rusli Abdullah

Research output: Contribution to journalArticle

Abstract

�Effective knowledge management system (KMS) should be able to deliver relevant knowledge to the right knowledge user at the right time. However, current KMS still largely relies on human efforts to access, extract and filter information pertinent to their knowledge need, resulted in inefficient process especially in collaborative learning environment. Effective KMS requires the identification of proper technology designed with the right system features to support the knowledge management (KM) activities to ensure that the goals of KM will be achieved. This study analyzed the proposed Semantic KMS Model for Collaborative Learning Environment using structural equation modelling (SEM) to test the effects of the model constructs in achieving the KM goals of KMS used in organizations. The model build upon comprehensive reviews of existing models in literature, and a prototype called Semantic Knowledge Management System for Collaborative Learning (SKMSCL) is developed to translate the constructs into KMS features. A post-implementation survey was conducted to assess the semantic KMS prototype in terms of the system quality, knowledge quality and the semantic KMS features identified, and how well the SKMSCL support the KM goals in comparison with the current KMS used in higher learning institutions (HLIs). Data was collected via questionnaire from a private university who participated in this study. Since there were no references can be found on the relationship between KMS knowledge quality, system quality and semantic KMS features and KM Goals, eleven research questions are derived from the model rather than hypotheses. In summary, findings indicated that seven out of eleven research questions tested are significant and supported by the findings.

Original languageEnglish
Pages (from-to)71-77
Number of pages7
JournalJournal of Telecommunication, Electronic and Computer Engineering
Volume9
Issue number2-9
Publication statusPublished - 01 Jan 2017

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Knowledge management
Semantics
Computer systems

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

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abstract = "�Effective knowledge management system (KMS) should be able to deliver relevant knowledge to the right knowledge user at the right time. However, current KMS still largely relies on human efforts to access, extract and filter information pertinent to their knowledge need, resulted in inefficient process especially in collaborative learning environment. Effective KMS requires the identification of proper technology designed with the right system features to support the knowledge management (KM) activities to ensure that the goals of KM will be achieved. This study analyzed the proposed Semantic KMS Model for Collaborative Learning Environment using structural equation modelling (SEM) to test the effects of the model constructs in achieving the KM goals of KMS used in organizations. The model build upon comprehensive reviews of existing models in literature, and a prototype called Semantic Knowledge Management System for Collaborative Learning (SKMSCL) is developed to translate the constructs into KMS features. A post-implementation survey was conducted to assess the semantic KMS prototype in terms of the system quality, knowledge quality and the semantic KMS features identified, and how well the SKMSCL support the KM goals in comparison with the current KMS used in higher learning institutions (HLIs). Data was collected via questionnaire from a private university who participated in this study. Since there were no references can be found on the relationship between KMS knowledge quality, system quality and semantic KMS features and KM Goals, eleven research questions are derived from the model rather than hypotheses. In summary, findings indicated that seven out of eleven research questions tested are significant and supported by the findings.",
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