A General Knowledge Representation Model of Concepts

Ramírez, Carlos; Valdes, Benjamin

doi:10.5772/37113

Cited by 14 publications

(10 citation statements)

References 51 publications

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“…Due to the various kinds of knowledge in m-learning system, using the transfer approach makes the maintenance process challenging and time-consuming. Thus, this method is only feasible for small scale prototypes, thereby ushering a shift from the transfer approach to the modeling approach (Ramirez & Valdes, 2012). The modeling approach is not intended to simulate the entire cognitive process of the student, but to produce a model which offers similar results.…”

Section: Knowledge Base Modeling In M-learningmentioning

confidence: 99%

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Al-Hunaiyyan

Bimba

Idris

et al. 2017

IJIKM

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Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

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Section: Knowledge Base Modeling In M-learningmentioning

confidence: 99%

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Al-Hunaiyyan

Bimba

Idris

et al. 2017

IJIKM

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“…In knowledge engineering and psychology, a category is known as a concept: "A concept is considered as the representation of a mental object and a set of attributes, expressed through a specific language of the mind which lets it be represented through symbols or patterns which are computable" [11]. To build information systems, conceptual modelling is an important part of the analysis phase.…”

Section: The Modelling Trianglementioning

confidence: 99%

The star pattern

Slotos

2016

Proceedings of the 21st European Conference on Pattern Languages of Programs

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This software architecture pattern describes how to set up an information system's architecture by using 'business components' and their 'representational connectors' that, together, form the Star pattern. The pattern allows the separation of business concerns from technology. The various representational connectors, which allow external systems to interact with a business component, are both non-invasive and loosely coupled to the business domain. These characteristics of the Star pattern support highly exchangeable interconnectivity channels. How the separation between a "business component" and its "representational connectors" can be established is described by using a linguistic view for how an information systems architecture can be structured.

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“…a) Survey: A preliminary study was carried out using a quantitative approach with the aim to get feedback from Malaysian teachers about their daily lesson planning. An online survey questionnaire was developed using Google Form 6 to facilitate faster distribution and administration involving a wider participation compared to the conventional survey questionnaire. Essentially, the procedure of the survey involved three main steps.…”

Section: ) Knowledge Acquisitionmentioning

confidence: 99%

“…Essentially, the purpose of understanding what knowledge is and what are the types of knowledge that exist allows us to use it in artificial systems [6]. Thus, in the context of lesson plan construction, the issue of deciding what is to be stored and how memory should be organized in order to retrieve and reuse previously prepared teaching plans effectively and efficiently needs to be addressed urgently.…”

Section: Introductionmentioning

confidence: 99%

The Methodology for Ontology Development in Lesson Plan Domain

Saad¹,

Shaharin²

2016

ijacsa

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Abstract-Ontology has been recognized as a knowledge representation mechanism that supports a semantic web application. The semantic web application that supports lesson plan construction is crucial for teachers to deal with the massive information sources from various domains on the web. Thus, knowledge in lesson plan domain needs to be represented accordingly so that the search on the web will retrieve relevant materials only. Essentially, such retrieval needs an appropriate representation of the domain problem. The emergence of semantic web technology provides a promising solution to improve the representation, sharing, and re-use of information to support decision making. Thus, the knowledge of lesson plan domain needs to be represented ontologically to support efficient retrieval of semantic web application in the domain of lesson plan. This paper presents a new methodology for ontology development representation of lesson plan domain to support semantic web application. The methodology is focused on the important model, tools, and techniques in each phase of the development. The methodology consists of four phases, namely requirements analysis, development, implementation, evaluation and maintenance.

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A General Knowledge Representation Model of Concepts

Cited by 14 publications

References 51 publications

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

The star pattern

The Methodology for Ontology Development in Lesson Plan Domain

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