A Model of a Weighted Agent System for Personalised E-Learning Curriculum

  • Ufuoma Chima ApokiEmail author
  • Soukaina Ennouamani
  • Humam K. Majeed Al-Chalabi
  • Gloria Cerasela Crisan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1126)


Progressive developments in the world of Information and Communications Technology open up many frontiers in the educational sector. One of such is adaptive e-learning systems, which is currently attracting a lot of research and development. Several conceptualisations and implementations rely on single parameters, or at most three or four parameters. This is not sufficient to account for the wide range of factors which can affect the learning process in an unconventional learning environment such as the web. Being able to choose relevant parameters for personalisation in different learning scenarios is vital to accommodate a wide range of these factors. In this paper, we’ll do a review of the basic concepts and components of an adaptive e-learning system. Afterwards, we’ll present a model of an adaptive e-learning system which generates a specialised curriculum for a learner based on a multi-parameter approach, thereby allowing for more choices in the process of creating a personalised and learner-oriented experience for such user. This will involve assembling (and/or suggesting) learning resources encompassed in a general curriculum and adapting it to specific personalities and preferences of users. The degree of adaptation (of the curriculum) is dependent on a weighted algorithm matching user features (relevant in each learning scenario) to the corresponding features of available learning resources.


Personalised online learning environments Personalisation parameters Personalised curriculum Software agents 


  1. 1.
    UNESCO Global Education Monitoring Report: Education for people and planet: Creating Sustainable Futures for All, Paris, France (2016)Google Scholar
  2. 2.
    Wiles, J.: Leading Curriculum Development, p. 2. Corwin Press, Thousand Oaks (2008)Google Scholar
  3. 3.
    Fischer, G.: User modeling in human-computer interaction. User Model. User Adapt. Interact. 11, 65–86 (2001). Scholar
  4. 4.
    Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z., Jain, L.C.: E-Learning Systems: Intelligent Techniques for Personalization. Springer, Switzerland (2017). CrossRefGoogle Scholar
  5. 5.
    Ennouamani, S., Mahani, Z.: An overview of adaptive e-learning systems. In: 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 342–347. IEEE (2017).
  6. 6.
    Chrysoulas, C., Fasli, M.: Building an adaptive e-learning system. In: Proceedings of the 9th International Conference on Computer Supported Education, pp. 375–382. SCITEPRESS - Science and Technology Publications (2017).
  7. 7.
    Essalmi, F., Ayed, L.J.B., Jemni, M., Kinshuk, Graf, S.: A fully personalization strategy of E-learning scenarios. Comput. Hum. Behav. 26, 581–591 (2010). Scholar
  8. 8.
    Boticario, J.G., Santos, O.C., Van Rosmalen, P.M.: Issues in developing standard-based adaptive learning management systems. Science (80), 2–5 (2005) Google Scholar
  9. 9.
    Phobun, P., Vicheanpanya, J.: Adaptive intelligent tutoring systems for e-learning systems. Procedia Soc. Behav. Sci. 2, 4064–4069 (2010). Scholar
  10. 10.
    Ennouamani, S., Mahani, Z.: Designing a practical learner model for adaptive and context-aware mobile learning systems. IJCSNS Int. J. Comput. Sci. Netw. Secur. 18, 84–93 (2018)Google Scholar
  11. 11.
    Vandewaetere, M., Desmet, P., Clarebout, G.: The contribution of learner characteristics in the development of computer-based adaptive learning environments. Comput. Hum. Behav. 27, 118–130 (2011). Scholar
  12. 12.
    Sampson, D., Karagiannidis, C., Kinshuk, C.: Personalised learning: educational, technological and standardisation perspective. Interact. Educ. Multimed. 4, 24–39 (2002)Google Scholar
  13. 13.
    Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007). Scholar
  14. 14.
    Hamada, M., Hassan, M.: An enhanced learning style index: implementation and integration into an intelligent and adaptive e-learning system. Eurasia J. Math. Sci. Technol. Educ. 13, 4449–4470 (2017). Scholar
  15. 15.
    Al-Hmouz, A., Shen, J., Yan, J., Al-Hmouz, R.: Enhanced learner model for adaptive mobile learning. In: Proceedings of the 12th International Conference on Information Integration and Web-based Applications and Services - iiWAS 2010, pp. 783–786. ACM Press, New York (2010).
  16. 16.
    Chang, Y.C., Kao, W.Y., Chu, C.P., Chiu, C.H.: A learning style classification mechanism for e-learning. Comput. Educ. 53, 273–285 (2009). Scholar
  17. 17.
    Red, E.R., Borlongan, H.G.S., Briagas, T.T., Mendoza, M.J.M.: Classification of Students Performance in a Learning Management System Using their eLearning Readiness Attributes. In: 2015 International Conference on e-Commerce, e-Administration, e-Society, e-Education, and e-Technology (e-CASE and e-Tech 2015), pp. 199–211 (2015)Google Scholar
  18. 18.
    Learning object standard - EduTech Wiki.
  19. 19.
    Guevara, C., Aguilar, J., González-Eras, A.: The model of adaptive learning objects for virtual environments instanced by the competencies. Adv. Sci. Technol. Eng. Syst. J. 2, 345–355 (2017). Scholar
  20. 20.
    Hammami, S., Mathkour, H.: Adaptive e-learning system based on agents and object Petri nets (AELS-A/OPN). Comput. Appl. Eng. Educ. 23, 170–190 (2015). Scholar
  21. 21.
    Serçe, F.C., Alpaslan, F.N., Jain, L.C.: Adaptive intelligent learning system for online learning environments. In: The Handbook on Reasoning-Based Intelligent Systems, pp. 353–387. World Scientific (2013). Scholar
  22. 22.
    Resource Description Framework (RDF) - W3C Semantic Web.
  23. 23.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78, 674–681 (1988)Google Scholar
  24. 24.
    Institute of Electrical and Electronics Engineers, IEEE Computer Society. Learning Technology Standards Committee, IEEE-SA Standards Board: IEEE Standard for Learning Object Metadata. Institute of Electrical and Electronics Engineers (2002)Google Scholar
  25. 25.
    Bellifemine, F., Caire, G., Trucco, T., Rimassa, G.: JADE programmer’s guide (2010)Google Scholar
  26. 26.
    Ennouamani, S., Akharraz, L., Mahani, Z.: Integrating ICT in education: an adaptive learning system based on users’ context in mobile environments. In: Farhaoui, Y., Moussaid, L. (eds.) ICBDSDE 2018. SBD, vol. 53, pp. 15–19. Springer, Cham (2019). Scholar
  27. 27.
  28. 28.
    D2RQ, Accessing Relational Databases as Virtual RDF Graphs.
  29. 29.
    SPARQL Query Language for RDF, W3C.
  30. 30.
    Protege, Stanford University.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ufuoma Chima Apoki
    • 1
    Email author
  • Soukaina Ennouamani
    • 2
  • Humam K. Majeed Al-Chalabi
    • 3
  • Gloria Cerasela Crisan
    • 1
    • 4
  1. 1.Faculty of Computer ScienceAlexandru Ioan Cuza UniversityIasiRomania
  2. 2.National School of Applied SciencesIbn Zohr UniversityAgadirMorocco
  3. 3.Faculty of Automatics, Computer Science and ElectronicsUniversity of CraiovaCraiovaRomania
  4. 4.Faculty of SciencesVasile Alecsandri University of BacauBacauRomania

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