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


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.


Ontologies E-Learning Personalization Adaptivity Content Recommender 



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.


  1. 1.
    Aamodt A, Plaza E (1994) Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Commun 7(1):39–59Google Scholar
  2. 2.
    Arditi D, Tokdemir B (1999) Comparison of Case-Based Reasoning and Artificial INeural Networks. J Comput Civ Eng 13(3):578–583CrossRefGoogle Scholar
  3. 3.
    Bates T (2005) Technology, e-learning and distance education. Routledge, LondonCrossRefGoogle Scholar
  4. 4.
    Bouquet P, Molinari A (2016) Using Semantic Technologies in E-Learning Platforms: a Case Study. International Journal of Information and Education Technology 6(5)Google Scholar
  5. 5.
    Bozkurt A, Akgun-Ozbek E et al (2015) Trends in Distance Education Research: A Content Analysis of Journals 2009-2013. Int Rev Res Open Dist Learn 16(1):330–363Google Scholar
  6. 6.
    Connolly P, Neill J (2011) Constructions of locality and gender and their impact on the educational aspirations of working class children. Int Stud Sociol Educ 11(2):107–130CrossRefGoogle Scholar
  7. 7.
    David P (2007) A Design Requirements Framework for Distance Learning Environments. J Comput 2(4):99–113Google Scholar
  8. 8.
    Ergen T, Kozat SS (2017) Neural Networks based Online Learning. In: IEEE 25th Signal Processing and Communications Applications Conference. Google Scholar
  9. 9.
  10. 10.
    Guarino N, Welty CA (2004) An Overview of OntoClean”, The Handbook on Ontologies Berlin: Springer-VerlagGoogle Scholar
  11. 11.
    Jahankhani H, Tawil R (2015) Adaptive E-learning Approach based on Semantic Web Technology. International Journal of Webology 10(2)Google Scholar
  12. 12.
    Kaur P (2015) Classification and prediction based on DM algorithm for slow learners. International Conference on Recent Trends in ComputingGoogle Scholar
  13. 13.
    Lafore R. Object-Oriented Programming in C++, Fourth Edition, Sams Publishing, Published December 29th 2001Google Scholar
  14. 14.
    Liem B, Beek W, Gracia J, Lozano E (2013) DynaLearn–An Intelligent Learning Environment for Learning Conceptual Knowledge. AI Mag 34(4):46–65CrossRefGoogle Scholar
  15. 15.
    Lin CC (2008) A case study on SCORM – based eLearning in computer aided drafting course with user satisfaction survey. WSEAS Trans Inf Sci Appl 5(10):1416–1427Google Scholar
  16. 16.
    Luis E, Rofio A et al (2013) A recommender system for educational resources in specific learning content. International conference on computer science and EducationGoogle Scholar
  17. 17.
    Mihăescu MC (2011) Classification of Learners Using Linear Regression. Proceedings of the Federated Conference on Computer Science and Information Systems pp. 717–721 ISBN 978-83-60810-22-4Google Scholar
  18. 18.
    Mohammad TZ, Mahmoud AM et al (2014) Classification Model of English Course e-Learning System for Slow Learners “Recent Advances in Information Science”. International Journal of Computer Science (IIJCS) , ISBN: 978-960-474-304-9Google Scholar
  19. 19.
  20. 20.
    Poveda-Villalón M et al (2012) Validating Ontologies with OOPS!. The 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW), pp. 267–281Google Scholar
  21. 21.
    Rani M, Srivastava KV, Vyas OP (2016) An Ontological Learning Management System. Comput Appl Eng Educ 24(5):706–722CrossRefGoogle Scholar
  22. 22.
    Romero C, Ventura S (2007) Educational Data Mining: A Survey from 1995 to 2005. Expert Syst Appl 33(1):135–146CrossRefGoogle Scholar
  23. 23.
    Safyan M, Qayyum Z, Sarwar S (2017) Context-Aware Personalized Activity Modeling in Concurrent Environment. Internet of Things (IoT)Google Scholar
  24. 24.
    Salam F, Shambour Q (2015) A Framework of semantic recommender system for e-learning. Journal of Software 10:317–330CrossRefGoogle Scholar
  25. 25.
    Saleena B, Srivastava K (2015) Using concept similarity in cross ontology for adaptive e-learning. Journal of King Saud University- Computer and Information Sciences 27(1):1–12CrossRefGoogle Scholar
  26. 26.
    Sarma Cakula MS (2013) Development of Personalized e-learning model. ICTE in Regional Development 26(4):113–120Google Scholar
  27. 27.
    Sarwar S, Ul Qayyum Z, Castro RG, Safyan M (2018) Ontology based E-learning Systems: A Step towards context aware content recommendation. International Journal of Information and Educational Technology 8(10):10–19CrossRefGoogle Scholar
  28. 28.
    Sarwar S, García-Castro R, Qayyum ZU, Safyan M (2017) Ontology-based Learner Categorization through Case Based Reasoning and Fuzzy Logic. In: International Conference on E-Learning (IADIS), pp 159–163Google Scholar
  29. 29.
    Sarwar S, Ul Qayyum Z, Safyan M, Munir F (2016) Ontology based Adaptive, Semantic E-Learning Framework (OASEF), Springer LNEE ICISAGoogle Scholar
  30. 30.
    Seteres V, Ossevroot MA et al (2012) Influence of student characteristics on use of adaptive e-learning material. Int Journal of Computers & Education 58(3):942–952CrossRefGoogle Scholar
  31. 31.
    Shen L, Shen R (2005) Ontology based Content Recommendation. International Journal of Continued Engineering and Education 15(1):13–26Google Scholar
  32. 32.
    Shute V, Towle B (2010) Adaptive e-learning. Educ Psychol 38(2):105–114CrossRefGoogle Scholar
  33. 33.
    Strumiłło P, Kamiński W (2013) Radial Basis Function Neural Networks: Theory and Applications, Neural Networks and Soft Computing. Advances in Soft Computing 19(5):107–119Google Scholar
  34. 34.
    Tambe S, Kadam G (2016) An Efficient framework for E-Learning Recommendation system using fuzzy Logic and Ontology. International Research Journal of Engineering and Technology (IRJET) 3(6):2062–2067Google Scholar
  35. 35.
    Vanbelle S (2016) A New Interpretation of the Weighted Kappa Coefficients. Psychometrica 81(2):399–410MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Viola SR, Graf S, Kinshuk TL(2006) Analysis of Felder-Silverman Index of Learning Styles by a Data-driven Statistical Approach. IEEE International Symposium on Multimedia (ISM'06), pp. 7695–7704Google Scholar
  37. 37.
    West DM, Learning M (2013) Transforming Education, Engaging Students and Improving Outcomes. International Journal of ICT, E-Management and E-Learning 4Google Scholar
  38. 38.
    Yarandi M (2013) A Personalized Adaptive E-Learning Approach Based On Semantic Web Technology. Journal of Webology 10(2):751–766Google Scholar
  39. 39.
    Yarandi M, Jahankhani H, Tawil A-RH (2013) A personalized adaptive e-learning approach based on semantic web technology. Webology 10(2)Google Scholar
  40. 40.
    Yathongchai C et al (2013) Leamer Classification Based on Learning Behavior and Performance. IEEE Conference on Open Systems (ICOS)Google Scholar

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© 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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