A sharable platform for e-learning based on cloud computing architecture

Wang, Chun Chia

doi:10.1504/ijcc.2013.050957

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…With numerous applications that develop with various system and database schema, produces a big data with heterogeneity problem on that environment. Different application system with numerous and heterogeneity information, data sources, databases system and data representation makes communication and integration process between this applications difficult to implemented [8], [10].…”

Section: Heterogeneity Aspects On Learning Environment 21 Heterogenmentioning

confidence: 99%

Analysis and Identification the Complexity of Data Heterogeneity on Learning Environment Using Ontology

et al. 2015

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Distributed and various systems on learning environment are the current issues to produce big data and heterogeneity data problem. Heterogeneity on learning environment is about numerous learning applications and various learning information to support a learning process in educational institutions IntroductionImplementation of Electronic system on learning environments is becoming popular and very important in today's scenario because of their flexibility, convenience and accessibility to support learning activities in traditional learning process [1], [2]. There is numerous and various application systems on learning environment from different function and with specific purpose, this is usually known as heterogeneity on learning environment. The heterogeneity may be the difference in: User interface, Platform, Application system, Database system, Data representation etc.The heterogeneity of data is a current issue in distributed and various information sources. Development of applications and information systems makes heterogeneity problems grow up and more complex, and from that problems need to find the best solution [3], [4]. Data on learning environment is increasingly grown up and becoming more meaningful to support learning activities [5], [6].Heterogeneity of data on learning environment is about different data representation and types of information or data in different and numerous applications to support a learning process in education institutions [7]. Different applications are develop for specific purposes based on function and feature that included on that applications [5]. A lot of applications developed on learning environment, such as Teaching and learning online application, Library application system, Question bank system, Student management and payment system, Academic information management system, Student registration system and subject course evaluation system. In this paper, researchers are using UTM (Universiti Teknologi Malaysia) as a case study to analyze the data heterogeneity problem on university environment. With numerous applications that develop with various system and database schema, produces a big data with heterogeneity problem on that environment.

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Section: Heterogeneity Aspects On Learning Environment 21 Heterogenmentioning

confidence: 99%

Analysis and Identification the Complexity of Data Heterogeneity on Learning Environment Using Ontology

et al. 2015

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“…Every application is provide specific learning information with particular purpose. In the context of learning environment, the source of information are come from various type of application systems such as teaching and learning online application called e-learning system, academic information management system, student management and payment system, Subject Courses evaluation system, student registration system, student grading system (SGS), library application system and many others, as called as learning applications which are heterogeneous in various aspects [2]. Heterogeneity aspects on learning environment are become more widely and various [3,4].…”

Section: Introductionmentioning

confidence: 99%

Data mapping process to handle semantic data problem on student grading system

Yunianta

Yusof

Bramantoro

et al. 2016

Int. J. Adv. Intell. Informatics

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Many applications are developed on education domain. Information and data for each application are stored in distributed locations with different data representations on each database. This situation leads to heterogeneity at the level of integration data. Heterogeneity data may cause many problems. One major issue is about the semantic relationships data among applications on education domain, in which the learning data may have the same name but with a different meaning, or learning data that has a different name with same meaning. This paper discusses on semantic data mapping process to handle semantic relationships problem on education domain. There are two main parts in the semantic data mapping process. The first part is the semantic data mapping engine to produce data mapping language with turtle (.ttl) file format as a standard XML file schema, that can be used for Local Java Application using Jena Library and Triple Store. The Turtle file contains detail information about data schema of every application inside the database system. The second part is to provide D2R Server that can be accessed from outside environment using HTTP Protocol. This can be done using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. To implement the semantic data process, this paper focuses on the student grading system in the learning environment of education domain. By following the proposed semantic data mapping process, the turtle file format is produced as a result of the first part of the process. Finally, this file is used to be combined and integrated with other turtle files in order to map and link with other data representation of other applications.

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“…Hadoop [13,14] is a cloud computing platform to develop and parallel process the large-scale data more easily and this platform mainly features strong dilatation, low cost, high efficiency and good reliability etc. Hadoop platform contains two parts: Hadoop distributed file system and MapReduce computing model.…”

Section: Introductionmentioning

confidence: 99%

Personalized Overseas Chinese Education Model Based on Map-Reduce Model of Cloud Computing

Huang

2016

Int. J. Emerg. Technol. Learn.

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Abstract-The rapid updates of the resources and media in the big data age provide new opportunities for oversea Chinese education. It is an urgent task to effectively use the big data to boost the development of oversea Chinese education. However, very few studies are conducted in this area. MapReduce is a programming model of cloud computing used for the parallel computing of the large-scale data sets and this model enables programmers to run their own programs in the distributed system. In this paper we proposed a personalized overseas Chinese education model based on MapReduce mechanism , which can analyze the behavioral habits and personal preferences of users from a large pool of Chinese educational resources. In this way, the customer needs can be accurately grasped and their favorite resources are recommended from huge amounts of resources. The proposed model has a good application prospect for overseas Chinese education .

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A sharable platform for e-learning based on cloud computing architecture

Cited by 5 publications

References 5 publications

Analysis and Identification the Complexity of Data Heterogeneity on Learning Environment Using Ontology

Analysis and Identification the Complexity of Data Heterogeneity on Learning Environment Using Ontology

Data mapping process to handle semantic data problem on student grading system

Personalized Overseas Chinese Education Model Based on Map-Reduce Model of Cloud Computing

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