Notice of Retraction: Using Ontological Engineering for Computer Education on Online E-learning Community System

Lo, Wei-Shuo; Chung, I-Chu; Hsu, Hun-Hui

doi:10.1109/icetc.2009.70

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…The performance of both evolutionary optimizers was compared favorably against that of the benchmarking shortest-distance optimizer on various actual courses, thus showing the effectiveness of our proposed and complete e-learning system framework for learning path optimization. More importantly, our proposal clearly demonstrates the importance of enhanced ontology analysis, utilizing the appropriate heuristic and rule selection for the overall performance of adaptive or personalized e-learning systems (Lo et al 2009) for learning analytics.…”

Section: Discussionmentioning

confidence: 82%

“…Most online e-learning systems (Chen et al 2008;Lo et al 2009) allow the learners or students to specify their own learners' profiles and learning styles. Yet these e-learning systems (Baosteel Talent Development Institute, 2013;CityU 2009;Monroe 2012) usually also demand the pre-requisite requirements between course modules and/or some relationship measures between the involved concepts to be explicitly specified by the course designers/instructors so that an optimizer will be ultimately employed to return an optimal learning path/sequence (Acampora et al 2008;Wong 2009;Tam et al 2012) of involved concepts or modules for each individual learner after considering his/her academic performance, educational background, learning interests, learner's profile, learning style, and possibly other learners' profiles of relevant groups/levels (Chen et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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A new framework of concept clustering and learning path optimization to develop the next-generation e-learning systems

2014

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Most existing e-learning systems strictly require the course instructors to explicitly input the pre-requisite requirements and/or some relationship measures between the involved concepts/modules such that an optimal learning path as a sequence of the involved concepts can be determined for a class or an individual after considering the student's academic performance, educational background, learning interests, learner profile, learning styles, etc. In some cases, the learning path is determined solely by human experts. Since human can be biased, the course instructor's views on the relations of the involved concepts/modules can be imprecise or even contradictory, thus prohibiting any logical deduction of an optimal learning path. Besides, human experts may ignore or possibly be confused by contradictory requirements in the real-world applications. Therefore, we propose a new and systematic framework to develop the next-generation e-learning systems that will perform an explicit semantic analysis on the course materials to extract the individual concepts, and then grouped by a heuristic-based concept clustering algorithm to compute the relationship measures as the basis for extracting the prerequisite requirements/constraints between the involved concepts. Lastly, an evolutionary optimizer will be invoked to return the optimal learning sequence after considering multiple experts' recommended learning sequences which may contain conflicting views in different cases. It is worth noting that our proposed and structured framework with the seamless integration of concept clustering and learning path optimization uniquely represents the first attempt to facilitate the course designers/instructors in providing more personalized and 'systematic' advice (2014) 1(4): 335-352 DOI 10.1007/s40692-014-0016-8 through optimizing the learning path(s) for each individual class/learner. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework, and also enhanced the original optimizer with the hill-climbing heuristic. Our empirical evaluation clearly revealed the many possible advantages of our proposal with interesting directions for future investigation.

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Section: Discussionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

A new framework of concept clustering and learning path optimization to develop the next-generation e-learning systems

2014

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“…The transformation from traditional learning to design the e-Learning system became very popular to gain explicit knowledge. A study by Lo (2011) focuses on traditional learning and e-Learning, by adopting a value-added process orientation and an ontological instructional system. In the same way, a platform architecture for e-learning was proposed by Kaur et al (2015), where their system consists of ontology for the e-learning process, such as teaching methods, learning styles, learning activities and course syllabus.…”

Section: Other Related Workmentioning

confidence: 99%

“…A value-adding instructional system is necessary for designing an e-Learning system. A study conducted by Lo (2011), where traditional learning and e-Learning is involved by adopting a process-oriented and systematic method of analysis. This study also draws a model called the value-added model for instructional system design for e-learning.…”

Section: Other Related Workmentioning

confidence: 99%

Reorganizing Educational Institutional Domain using Faceted Ontological Principles

Das¹,

Naskar²,

Roy³

2022

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The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as, Identification of the terminology, Analysis, Synthesis, Standardization and Ordering. Firstly, for purposes of conceptualization OntoUML has been used which is a well-founded and established language for Ontology driven Conceptual Modelling. Phase transformation of “the same mode” has been subsequently obtained by OWL-DL using Protégé software. The final OWL ontology contains a total of around 232 axioms. These axioms comprise 148 logical axioms, 76 declaration axioms and 43 classes. These axioms glue together classes, properties and data types as well as a constraint. Such data clustering cannot be achieved through general use of simple classification schemes. Hence it has been observed and established that domain ontology using faceted principles provide better information retrieval with enhanced precision. This ontology should be seen not only as an alternative of the existing classification system but as a Knowledge Base (KB) system which can handle complex queries well, which is the ultimate purpose of any classification system or indexing system. In this paper, we try to understand how ontology-based information retrieval systems can prove its utility as a useful tool in the field of library science with a particular focus on the education domain.

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Developing Adaptive and Personalized Distributed Learning Systems with Semantic Web Supported Multi Agent Technology

Çiloğlugil

İnceoğlu

2010

2010 10th IEEE International Conference on Advanced Learning Technologies

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Notice of Retraction: Using Ontological Engineering for Computer Education on Online E-learning Community System

Cited by 5 publications

References 2 publications

A new framework of concept clustering and learning path optimization to develop the next-generation e-learning systems

A new framework of concept clustering and learning path optimization to develop the next-generation e-learning systems

Reorganizing Educational Institutional Domain using Faceted Ontological Principles

Developing Adaptive and Personalized Distributed Learning Systems with Semantic Web Supported Multi Agent Technology

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