A Framework for a Knowledge Management System in a Cloud Computing Environment Using a Knowledge Engineering Approach

Anupan, Anuchit; Nilsook, Prachyanun; Wannapiroon, Panita

doi:10.7763/ijke.2015.v1.25

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…The conceptualization and application of cloud computing in education support an evolution from information and communication technology that pushes out information, to a knowledge management system that facilitates the creation and sharing of knowledge (Hoong & Lim, 2012). As described by Anupan, Nilsook, and Wannapiroon (2015), knowledge management systems are supported by (a) Internet access, (b) tools for gathering information, (c) database management systems that are accessible through the network, (d) expert systems that are continuously available to create knowledge, and (e) tools for communicating and collaborating within an organization. Despite many arguing that cloud computing offers important opportunities for educators to access and share data in time and cost efficient ways, much of the potential that exists for use of cloud computing tools as knowledge management systems has yet to be realized in school systems (Sultan, 2013).…”

Section: Introduction To the Problemmentioning

confidence: 99%

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

Johnson

2017

Topics in Early Childhood Special Education

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Multi-tiered systems of support (MTSS) provide frameworks to guide the instructional decisions and problem solving of educators (i.e., Sugai & Horner, 2009). Within MTSS, the availability of ongoing child assessment and progress monitoring data is described as a critical feature (Buzhardt et al., 2010; Sugai & Horner, 2009). For some time, there was an assumption that if tools were created to gather information about children's proficiency with and progress in developing essential learning targets, educators would be equipped to effectively engage in two essential components of instruction, data-driven decision making and ongoing problem solving (Hoogland et al., 2016; Marsh & Farrell, 2015; Todd et al., 2011). Unfortunately, multiple researchers have demonstrated that despite the availability of tools for gathering data, those data do not always lead to decision making about instructional practices that are in service of improving child outcomes (Heritage, Kim, Vendlinski, & Herman, 2009; Olah, Lawrence, & Riggan, 2010). The purpose of this article is to explore one approach to trying to address the problems associated with data-driven decision making in early childhood through use of cloudbased tools for gathering and summarizing data. To begin that exploration, a brief description will be provided that grounds the work in critical efforts to address the social, emotional, and behavioral development of young children. The complexities of the work in early childhood will then be described as a means for considering why existing tools may not be sufficient when promoting data-based problem solving. Then, a model for team-based problem solving will be described as a necessary approach to building knowledge about the use of data that meaningfully supports instructional decisions. Finally, a cloud-based knowledge management system will be described, along with the experience of a group of early childhood programs using the system, to demonstrate the potential benefits that that type of system may offer when the goal is to promote actual use of data, not just the gathering of data. Actualizing the use of data for effective decision making is a multifaceted issue. In addition to the barriers that are often expected during the installation of new practices, such as time and expertise (Anderson,

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Section: Introduction To the Problemmentioning

confidence: 99%

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

Johnson

2017

Topics in Early Childhood Special Education

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“…This means that this study can be applied to future developments of the KRMS-SWE and tested on undergraduates. This corresponds with the theories of Chang and Zhang [17], Anupan [18], Anupan, Nilsook and Wannapiroon [19] and Chotijiraphan [20]. It comprises four components as follows:…”

Section: Discussionmentioning

confidence: 94%

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

Thanachawengsakul¹,

Wannapiroon²,

Nilsook³

2019

Int. J. Emerg. Technol. Learn.

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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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“…Knowledge repository management system is system that is developed in a form of information and communication technology via internet featuring functions of member management, knowledge sharing, social and business network & current information [6] to increase efficiency of knowledge acquisition processes for users so that they can publicize, disseminate and transfer knowledge to the system where they can access anywhere and anytime. Moreover, the system is compatible with all devices [7], [8].…”

Section: Knowledge Repository Managementmentioning

confidence: 99%

Synthesis of Digital Knowledge Engineering Repository Management System

Thanachawengsakul¹,

Wannapiroon²,

Nilsook³

2019

IJEEEE

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Knowledge management in educational organizations is crucial and important that should be promoted and supported so as to lead teachers and learners to develop body of knowledge as best practice for facilitating and applying development through contemporary digital technology. The aim of this research was therefore (1) synthesis of digital knowledge engineering repository management system and (2) suitability evaluation regarding synthesis of digital knowledge engineering repository management system. In this study, the researchers used data analysis and synthesis methods approached from content analysis whereas the suitability evaluation of the synthesis was performed by five experts having a minimum of three years of experience in related field. The findings of the research suggested that (1) digital knowledge engineering repository management system consisted of (1.1) user management system, (1.2) knowledge management system and (1.3) learning management system in which knowledge management process on digital knowledge engineering was conducted in collaboration with knowledge verification on machine learning to increase efficiency of knowledge management, and (2) the result of suitability evaluation regarding synthesis of digital knowledge engineering repository management system was at the highest level (X =4.92, S.D. =0.27) which indicated that this study can be applied to development of knowledge repository management system

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A Framework for a Knowledge Management System in a Cloud Computing Environment Using a Knowledge Engineering Approach

Cited by 13 publications

References 25 publications

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

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

Synthesis of Digital Knowledge Engineering Repository Management System

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