Design of a performance-oriented workplace e-learning system using ontology

Jia, Haiyang; Wang, Minhong; Ran, Weijia; Yang, Stephen J.H.; Liao, Jian; Chiu, Dickson K.W.

doi:10.1016/j.eswa.2010.08.122

Cited by 64 publications

(33 citation statements)

References 32 publications

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“…Online learning is widespread in today's educational environments, supported by Virtual Learning Environments such as Moodle™, Sakai™, or Blackboard™ (Del Blanco, Marchiori, Torrente, Martínez-Ortiz, & Fernández-Manjón, 2013) and also may be referred to as e-learning, e-training, or web-based instruction (Colorado & Eberle, 2010). E-learning is also emerging as a popular learning approach utilized by many organizations (Jia et al, 2011). Learners can access the online resource and discuss with instructors or members in any location, at any time.…”

Section: Introductionmentioning

confidence: 99%

A model for assessing the impact of e-learning systems on employees’ satisfaction

Navimipour

Zareie

2015

Computers in Human Behavior

169

105

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Section: Introductionmentioning

confidence: 99%

A model for assessing the impact of e-learning systems on employees’ satisfaction

Navimipour

Zareie

2015

Computers in Human Behavior

169

105

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“…It refers to the use of computer technology to deliver information and instruction to individuals, it is also evolving into systems consisting of a variety of technologies [11]. Moreover, E-Learning enables to adapt learning content in a more efficient way by using its recommendation side.…”

Section: Recommendations In An E-learning Environmentmentioning

confidence: 99%

Personalized E-Learning Environment based on Ontology to Improve Students' School Levels

Alimam¹,

Seghiouer²

2013

IJCA

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Personalization of learning is the core of current research in the E-learning area. For effective learning, a personalized content provided to each student remains necessary. Students who use this educational web-based applications aim at learning knowledge which corresponds to their expectations. However, their school level and interests are different. Thus, the system should recommend to students a learning content that match the most their knowledge levels and is interesting enough to keep their attention. This paper describe a method enabling system to classify students depending on their school levels. This method will allow a semantic classification of profiles integrated within ontology. Accordingly, the system will be able to categorize students in a definite profile based on their school level and make a decision about the interfaces content recommended by each type of profile. To evaluate the effectiveness of the proposed approach, an experiment was conducted on students from middle school. The experimental results show that, such a system recommends to each student a field of study (FoS) whose subjects taught will match the best their skills. The E-Learning content based on those subjects will allow the enhancement of student's school level.

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“…The key performance indicators (KPIs) are put in place to spell out the organisational training requisites, and lend a helping hand to the learners to set up logical rational learning goals. Further, the ontology is efficiently employed for formulating the official and machine-friendly conceptualisation of the performance-oriented learning scenarios (Jia et al, 2010;Juan et al, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation

Muruganandam

Srininvasan

2017

IJBIDM

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Abstract:The ever-zooming issue of personalisation in the domain of the e-learning or the adaptive e-learning has emerged as a hot subject of intriguing debate among the inquisitive investigators in the last few years. In the learning contents module, all the critical learning materials in the shape of text, video or audio are collected and saved by means of the data management approach so as to effectively orchestrate the entire learning material. In the profile ontology module, the learner profile is saved as the ontology with a clear-cut framework and data. The Markov model is also utilised in this regard. In the instructor module, the online tutoring for each and every learner in accordance with the protocol devised by means of the captioned techniques is carried out. The execution is performed through the JAVA programming and protégé device and the performance is subjected to assessment.Keywords: e-learning; prefix-span algorithm; graphical user interface; dataset description.Reference to this paper should be made as follows: Muruganandam, S. and Srininvasan, N. (2017) 'Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation', Int.

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Design of a performance-oriented workplace e-learning system using ontology

Cited by 64 publications

References 32 publications

A model for assessing the impact of e-learning systems on employees’ satisfaction

A model for assessing the impact of e-learning systems on employees’ satisfaction

Personalized E-Learning Environment based on Ontology to Improve Students' School Levels

Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation

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Design of a performance-oriented workplace e-learning system using ontology

Cited by 64 publications

References 32 publications

A model for assessing the impact of e-learning systems on employees’ satisfaction

A model for assessing the impact of e-learning systems on employees’ satisfaction

Personalized E-Learning Environment based on Ontology to Improve Students&apos; School Levels

Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation

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Personalized E-Learning Environment based on Ontology to Improve Students' School Levels