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

  • Sohail SarwarEmail author
  • Zia Ul Qayyum
  • Raúl García-Castro
  • Muhammad Safyan
  • Rana Faisal Munir
Article
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Abstract

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.

Keywords

Ontologies E-Learning Personalization Adaptivity Content Recommender 

Notes

Acknowledgements

Authors of this manuscript are thankful for the support of the Dr. Muddessar Iqbal from London South Bank University (LSBU) England and Ontology Engineering Group (OEG) in unconditional support and guidance for completing this research. Also, we wish to acknowledge our gratitude to the Higher Education Commission (HEC) of Pakistan in extending its resources in completing this work.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ComputingUniversity of GujratGujratPakistan
  2. 2.Universidad Politecnica de Madrid (UPM)MadridSpain
  3. 3.Universidad Politecnica de CatalanBarcelonaSpain

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