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Adaptive e-Learning Systems Foundational Issues of the ADAPT Project

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Computational Intelligence and Decision Making

Abstract

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.

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References

  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 8–10

    Google Scholar 

  2. Ritu D, Sugata M (1999) Learning styles and perceptions of self. Int Educ J 1(1):61–71

    Google Scholar 

  3. Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. Artif Int Commun 7:39–52

    Google Scholar 

  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-X

    Google Scholar 

  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, 19–34

    Google Scholar 

  6. Myers IB, McCaulley MH (1985) Manual: a guide to the development and use of the Myers-Briggs type indicator. Consulting Psychologists Press, Palo Alto

    Google Scholar 

  7. Schmeck RR (1983) Learning styles of college students. In: Dillon R, Schmeck R (eds) Individual differences in cognition. Academic, New York

    Google Scholar 

  8. Kolb D (1984) Experiential learning: experience as the source of learning and development. Prentice-Hall, Englewood

    Google Scholar 

  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 87:213–223

    Article  Google Scholar 

  10. Mamdani EH, e Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13

    Article  MATH  Google Scholar 

  11. SHAI – Stottler Henke Artificial Intelligence (2003) White papers, www.shai.com

  12. Felder RM, Silverman LK (1988) Learning and teaching styles in engineering education [Electronic Version]. Eng Educ 78(7):674–681

    Google Scholar 

  13. European Comission – Education and Training (2009) http://ec.europa.eu/education/lifelong-learning-policy/doc28_en.htm. Last accessed on 10 Sept 2012

  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 67–69

    Google Scholar 

  15. Felder RM, Spurlin J (2005) Applications, reliability, and validity of the index of learning styles [Electronic Version]. Int J Eng Educ 21(1):103–112

    Google Scholar 

  16. VARK – A guide to learning styles (2010) http://www.vark-learn.com/english/index.asp. Last accessed on 10 Sept 2012

  17. Jensen D, Goldberg H (1998) Papers of the AAAI fall symposium on AI and link analysis, AAAI Press, Menlo Park

    Google Scholar 

  18. Mamdani EH (1973) Aplications of fuzzy algorithms for control of simple dynamic plant. Proc IEEE 12:1585–1588

    Google Scholar 

  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 68–73

    Google Scholar 

  20. Friesen N, McGreal R (2005) CanCore: best practices for learning object metadata in ubiquitous computing environments. IEEE Computer Society, Washington, DC

    Google Scholar 

  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 14–18

    Google Scholar 

  22. Keefe JW (1989) Learning style profile handbook: accommodating perceptual, study and instructional preferences, vol II. National Association of Secondary School Principals, Reston

    Google Scholar 

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Acknowledgments

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).

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Correspondence to Eduardo Pratas or Viriato M. Marques .

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© 2013 Springer Science+Business Media Dordrecht

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Pratas, E., Marques, V.M. (2013). Adaptive e-Learning Systems Foundational Issues of the ADAPT Project. In: Madureira, A., Reis, C., Marques, V. (eds) Computational Intelligence and Decision Making. Intelligent Systems, Control and Automation: Science and Engineering, vol 61. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4722-7_40

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  • DOI: https://doi.org/10.1007/978-94-007-4722-7_40

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-4721-0

  • Online ISBN: 978-94-007-4722-7

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