Art. 05 – Vol. 21 – No. 4 – 2011

Adaptive e-Learning Systems with Concept Maps

Rodica Potolea
Camelia Lemnaru
Florin Trif
Technical University of Cluj-Napoca

Abstract: Adaptive e-learning systems represent a new paradigm in modern learning approaches. They are not only targeting curricula segmentation, as providing large quantities of content is not the ultimate goal, but focus on the quality of knowledge transfer.  In doing so, the correct identification of the user learning style in order to provide the appropriate content presentation to each individual user is essential. Moreover, a continuous re-evaluation and classification is important to cope with the progress made during the learning process, and to ensure the evolution to a better style. Concept maps represent useful instruments in both developing quality high structured content and automated evaluation. This paper presents a model for an adaptive e-learning system, and details the modules responsible for the user type identification and concept maps.

Keyword: Adaptive e-learning, learning style, concept maps, clustering, self-organizing maps.


View full article



  1. CAÑAS, A. J.; REISKA, P.; ÅHLBERG, M.; NOVAK, J. D. (EDS.): Concept Mapping: Connecting Educators Proc. of the Third Int. Conference on Concept Mapping Tallinn, Estonia & Helsinki, Finland 2008.
  2. CHANG, Y.C.; KAO, W.Y.; CHU, C. P.; CHIU, C. H.: A learning style classification mechanism for e-learning. Computers & Education, article in press, journal homepage: 2009.
  3. CHEN, C. M.; LEE, H. M.; CHEN, Y. H.: Personalized e-learning system using Item Response Theory. Computers and Education, 44(3), 2005, pp. 237-255.
  4. FIRTE, A. A.; VIDRIGHIN BRATU, C.; CENAN, C.: Intelligent component for adaptive E-learning systems. Proceedings of the IEEE 5th International Conference on Intelligent Computer Communication and Processing, ICCP 2009, 27-29 August 2009, Cluj-Napoca, Romania, 2009, pp. 35-38.
  5. LEMNARU, C.; FIRTE, A. A.; POTOLEA, R.: Static and Dynamic User Type Identification in Adaptive E-learning with Unsupervised Methods. Proceedings of ICCP 2011, 2011, pp.11-18.
  6. FIRTE, A.A.: Metode Nesupervizate de Identificare a Sabloanelor in Date. Identificarea Tipologiei Utilizator in sisteme e-Learning Adaptive. Master thesis, Technical University of Cluj-Napoca, July 2011.
  7. GALE, D.; SHAPLEY, L. S.: College Admissions and the Stability of Marriage. American Mathematical Monthly 69, 1962, pp. 9-14.
  8. GARCÍA, P.; AMANDI, A.; SCHIAFFINO, S.; CAMPO, M.: Evaluating Bayesian Networks’ precision for detecting students’ learning styles. Computers and Education, 49(3), 2007, pp. 794-808.
  9. HLAOUI, A.; WANG, S.: Image Retrieval Systems Using Graph Matching. Rapport de Recherche, No. 275, Département de mathématiques et d’informatique, Univérsité de Sherbrooke, 2001.
  10. KRITIKOU, Y.; DEMESTICHAS, P.; ADAMOPOULOU, E.; DEMESTICHAS, K.; THEOLOGOU, M.; PARADIA, M.: User Profile Modeling in the context of web-based learning management systems. Journal Network. Comput. Appl.31, 4 (Nov. 2008), 2008, pp. 603-627.
  11. KUHN, HAROLD W.: Variants of the Hungarian method for assignment problems. Naval Research Logistics Quarterly, 3: 253-258, 1956.
  12. MERA, V.: Compararea Automata a Hartilor Conceptuale. Evaluarea in Sisteme e-Learning Adaptive. Master thesis, Technical University of Cluj-Napoca, July 2011.
  13. MITCHEL, T.; CHEN, S.Y.; MACREDIE, R.: Adapting hypermedia to cognitive Styles: it is necessary? Individual differences in Adaptive Hypermedia. Proceedings of the AH Workshop, 2004, pp. 70-79.
  14. MÖDRITSCHER, F.: Adaptive E-Learning Environments: Theory, Practice, and Experience. VDM Verlag, 2008.
  15. NOVAK, J. D.; CAÑAS, A. J.: The Theory Underlying Concept Maps and How to Construct Them. Technical Report IHMC CmapTools 2006-01 Rev 01-2008, Florida Institute for Human and Machine Cognition, 2008, available at: ResearchPapers/TheoryUnderlyingConceptMaps.pdf
  16. PAPANIKOLAOU, K. A.; GRIGORIADOU, M.: Accommodating learning styles characteristics in Adaptive Educational Hypermedia Systems. Individual differences in Adaptive Hypermedia, Proceedings of the AH Workshop, 2004, pp. 1-11.
  17. TRIF, F.; LEMNARU, C.; POTOLEA, R.: Identifying the User Typology for Adaptive E-learning Systems. Proceedings of  AQTR 2010 – IEEE International Conference on Automation, Quality and Testing, Robotics 2010, Tome III, 2010, pp. 192-198.
  18. SCHIAFFINO, S.; GARCIA, P.; AMANDI, A.: eTeacher: Providing personalized assistance to e-learning students. Computers & Education, 51, 2008, pp. 1744-1754.
  19. WITKIN, H. A.; MOORE, C. A.; GOODENOUGH, D. R.; COX, W.: Field dependent and field independent cognitive styles and their educational implications. Review of Educational Research, 47, 1977, pp. 1-64.
  20. WOOLF, B.P.: Building intelligent interactive tutors. Burlington, Elsevier: Morgan Kaufman Publisher, 2009, pp. 44-45

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.