Adaptive e-Learning Systems Foundational Issues of the ADAPT Project

  • Eduardo Pratas
  • Viriato M. Marques
Conference paper
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 61)


This paper presents some foundational issues for the design and implementation of adaptive e-learning systems and in particular their application to the ADAPT project. As a matter of fact, the current Learning Management Systems (LMS’s) lack pedagogy and interactivity, giving rise to e-learning models that strongly reside on student’s own motivation. Some Artificial Intelligence (AI) models and techniques can help to overcome this problem and turn future LMS’s into (almost) human teachers.


Learning Style Linguistic Term Learning Preference Intelligent Tutoring System Page Rank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank FCT – Fundação para a Ciência e Tecnologia - by funding the ADAPT Project – PTDC/CPE-CED/115175/2009 and FEDER – Eixo I of Programa Operacional Factores de Competitividade (POFC)/QREN (COMPETE: FCOMP-01-0124-FEDER-014418).


  1. 1.
    Benjamin B et al (1956) The taxonomy of educational objectives. The classification of educational goals, Handbook I: cognitive domain. David mckay Company, Inc., New York, pp 8–10Google Scholar
  2. 2.
    Ritu D, Sugata M (1999) Learning styles and perceptions of self. Int Educ J 1(1):61–71Google Scholar
  3. 3.
    Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. Artif Int Commun 7:39–52Google Scholar
  4. 4.
    Myers, Isabel Briggs with Peter B. Myers (Original edition 1980; Reprint edition 1995) Gifts differing: understanding personality type. Davies-Black Publishing, Palo Alto, 248 p. ISBN 0-89106-074-XGoogle Scholar
  5. 5.
    Zadeh LA (1996) Fuzzy sets, fuzzy logic and fuzzy systems – selected papers by Zadeh LA 1965–1996, Advances in fuzzy systems – applications and theory, vol 6. World Scientific, Singapore, 19–34Google Scholar
  6. 6.
    Myers IB, McCaulley MH (1985) Manual: a guide to the development and use of the Myers-Briggs type indicator. Consulting Psychologists Press, Palo AltoGoogle Scholar
  7. 7.
    Schmeck RR (1983) Learning styles of college students. In: Dillon R, Schmeck R (eds) Individual differences in cognition. Academic, New YorkGoogle Scholar
  8. 8.
    Kolb D (1984) Experiential learning: experience as the source of learning and development. Prentice-Hall, EnglewoodGoogle Scholar
  9. 9.
    Reichmann SW, Grasha AF (1974) A rational approach to developing and accessing the construct validity of a student learning style scale instrument. J Psychol 87:213–223CrossRefGoogle Scholar
  10. 10.
    Mamdani EH, e Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13zbMATHCrossRefGoogle Scholar
  11. 11.
    SHAI – Stottler Henke Artificial Intelligence (2003) White papers,
  12. 12.
    Felder RM, Silverman LK (1988) Learning and teaching styles in engineering education [Electronic Version]. Eng Educ 78(7):674–681Google Scholar
  13. 13.
    European Comission – Education and Training (2009) Last accessed on 10 Sept 2012
  14. 14.
    Anderson et al (2001) A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives. Longman, New York, pp 67–69Google Scholar
  15. 15.
    Felder RM, Spurlin J (2005) Applications, reliability, and validity of the index of learning styles [Electronic Version]. Int J Eng Educ 21(1):103–112Google Scholar
  16. 16.
    VARK – A guide to learning styles (2010) Last accessed on 10 Sept 2012
  17. 17.
    Jensen D, Goldberg H (1998) Papers of the AAAI fall symposium on AI and link analysis, AAAI Press, Menlo ParkGoogle Scholar
  18. 18.
    Mamdani EH (1973) Aplications of fuzzy algorithms for control of simple dynamic plant. Proc IEEE 12:1585–1588Google Scholar
  19. 19.
    BolzanW, Giraffa L (2002) Estudo Comparativo sobre Sistemas Tutores Inteligentes Multiagentes Web, Technical report Nº24, PUCRS Faculdade de Informática, BrasilJ. Clerk Maxwell, A treatise on electricity and magnetism, 3rd ed, vol 2. Clarendon, Oxford, 1892, pp 68–73Google Scholar
  20. 20.
    Friesen N, McGreal R (2005) CanCore: best practices for learning object metadata in ubiquitous computing environments. IEEE Computer Society, Washington, DCGoogle Scholar
  21. 21.
    Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine, Seventh international World-Wide Web conference (WWW 1998), Brisbane, Australia, April 14–18Google Scholar
  22. 22.
    Keefe JW (1989) Learning style profile handbook: accommodating perceptual, study and instructional preferences, vol II. National Association of Secondary School Principals, RestonGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.ISEC – Instituto Superior de Engenharia de CoimbraIPC – Polytechnic Institute of CoimbraCoimbraPortugal

Personalised recommendations