Personalized Students’ Profile Based On Ontology and Rule-based Reasoning
Nowadays, most of the existing e-learning architecture provides the same content to all learners due to ”one size fits for all” concept. E-learning refers to the utilization of electronic innovations to convey and encourage training anytime and anywhere. There is a need to create a personalized envir...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2016-12-01
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| Series: | EAI Endorsed Transactions on e-Learning |
| Subjects: | |
| Online Access: | http://eudl.eu/doi/10.4108/eai.2-12-2016.151720 |
| _version_ | 1828860220254715904 |
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| author | Shaimaa Nafea Leandros A. Maglaras Francois iewe Richard Smith Helge Janicke |
| author_facet | Shaimaa Nafea Leandros A. Maglaras Francois iewe Richard Smith Helge Janicke |
| author_sort | Shaimaa Nafea |
| collection | DOAJ |
| description | Nowadays, most of the existing e-learning architecture provides the same content to all learners due to ”one size fits for all” concept. E-learning refers to the utilization of electronic innovations to convey and encourage training anytime and anywhere. There is a need to create a personalized environment that involves collecting a range of information about each learner. Questionnaires are one way of gathering information on learning style, but there are some problems with their usage, such as reluctance to answer questions as well as guesses the answer being time consuming. Ontology-based semantic retrieval is a hotspot of current research, because ontologies play a paramount part in the development of knowledge. In this paper, a novel way to build an adaptive ontological student profile by analysis of learning patterns through a learning management system, according to the Felder-Silverman learning style model (FSLSM) and Myers-Briggs Type Indicator (MBTI) theory is proposed. |
| first_indexed | 2024-12-13T02:33:54Z |
| format | Article |
| id | doaj.art-6c4676c0d1de454699dba5a528fcf843 |
| institution | Directory Open Access Journal |
| issn | 2032-9253 |
| language | English |
| last_indexed | 2024-12-13T02:33:54Z |
| publishDate | 2016-12-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on e-Learning |
| spelling | doaj.art-6c4676c0d1de454699dba5a528fcf8432022-12-22T00:02:26ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on e-Learning2032-92532016-12-0131211810.4108/eai.2-12-2016.151720Personalized Students’ Profile Based On Ontology and Rule-based ReasoningShaimaa Nafea0Leandros A. Maglaras1Francois iewe2Richard Smith3Helge Janicke4School Of Business, Arab Academy For Science Technology & Maritime Cairo, EgyptSchool of Computer Science and Informatics De Montfort University Leicester, UK; leandros.maglaras@dmu.ac.ukSchool of Computer Science and Informatics De Montfort University Leicester, UKSchool of Computer Science and Informatics De Montfort University Leicester, UKSchool of Computer Science and Informatics De Montfort University Leicester, UKNowadays, most of the existing e-learning architecture provides the same content to all learners due to ”one size fits for all” concept. E-learning refers to the utilization of electronic innovations to convey and encourage training anytime and anywhere. There is a need to create a personalized environment that involves collecting a range of information about each learner. Questionnaires are one way of gathering information on learning style, but there are some problems with their usage, such as reluctance to answer questions as well as guesses the answer being time consuming. Ontology-based semantic retrieval is a hotspot of current research, because ontologies play a paramount part in the development of knowledge. In this paper, a novel way to build an adaptive ontological student profile by analysis of learning patterns through a learning management system, according to the Felder-Silverman learning style model (FSLSM) and Myers-Briggs Type Indicator (MBTI) theory is proposed.http://eudl.eu/doi/10.4108/eai.2-12-2016.151720adaptive LearningSemantic W ebA daptabilityLearner ProfileontologyPellet reasonerFSLSMMBTI |
| spellingShingle | Shaimaa Nafea Leandros A. Maglaras Francois iewe Richard Smith Helge Janicke Personalized Students’ Profile Based On Ontology and Rule-based Reasoning EAI Endorsed Transactions on e-Learning adaptive Learning Semantic W eb A daptability Learner Profile ontology Pellet reasoner FSLSM MBTI |
| title | Personalized Students’ Profile Based On Ontology and Rule-based Reasoning |
| title_full | Personalized Students’ Profile Based On Ontology and Rule-based Reasoning |
| title_fullStr | Personalized Students’ Profile Based On Ontology and Rule-based Reasoning |
| title_full_unstemmed | Personalized Students’ Profile Based On Ontology and Rule-based Reasoning |
| title_short | Personalized Students’ Profile Based On Ontology and Rule-based Reasoning |
| title_sort | personalized students profile based on ontology and rule based reasoning |
| topic | adaptive Learning Semantic W eb A daptability Learner Profile ontology Pellet reasoner FSLSM MBTI |
| url | http://eudl.eu/doi/10.4108/eai.2-12-2016.151720 |
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