Ontology based E-learning framework: A personalized, adaptive and context aware model

Enhancing the degree of learner productivity, one of the major challenges in E-Learning systems, may be catered through effective personalization, adaptivity and context awareness while recommending the learning contents to the learners. In this paper, an E-Learning framework has been proposed that...

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Bibliographic Details
Published in:Multimedia tools and applications 2019-12, Vol.78 (24), p.34745-34771
Main Authors: Sarwar, Sohail, Qayyum, Zia Ul, García-Castro, Raúl, Safyan, Muhammad, Munir, Rana Faisal
Format: Article
Language:English
Subjects:
Classification
Computer Communication Networks
Computer Science
Customization
Data Structures and Information Theory
Distance learning
Evaluation
Machine learning
Mathematical models
Multimedia Information Systems
Neural networks
Online instruction
Ontology
Productivity
Special Purpose and Application-Based Systems
System effectiveness
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Summary:Enhancing the degree of learner productivity, one of the major challenges in E-Learning systems, may be catered through effective personalization, adaptivity and context awareness while recommending the learning contents to the learners. In this paper, an E-Learning framework has been proposed that profiles the learners, categorizes the learners based on profiles, makes personalized content recommendations and performs assessment based content adaptation. A mathematical model has been proposed for learner categorization using machine learning techniques (a hybrid of case based reasoning and neural networks). The learning contents have been annotated through CourseOntology in which three academic courses (each for language of C++, C# and JAVA) have been modeled for the learners. A dynamic rule based recommender has been presented targeting a ‘relative grading system’ for maximizing the learner’s productivity. Performance of proposed framework has been measured in terms of accurate learner categorization, personalized recommendation of the learning contents, completeness and correctness of ontological model and overall performance improvement of learners in academic sessions of 2015, 2016 and 2017. The comparative analysis of proposed framework exhibits visibly improved results compared to prevalent approaches. These improvements are signified to the comprehensive attribute selection in learner profiling, dynamic techniques for learner categorization and effective content recommendation while ensuring personalization and adaptivity.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08125-8