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Information Adaptation by an Intelligent Knowledge-Oriented Mechanism

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Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 364))

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Abstract

Herein, we present a mechanism used to adapt delivered information and oriented onto knowledge. It was deployed within the e-learning software platform and its content. This mechanism was developed and tested in a distributed learning environment, but its capabilities are not limited to this domain. As the e-learning is based on various pieces of software, it is possible to efficiently gather data and extract meaningful information about learner’s needs. Together with the delivered knowledge about course, we can use them in the reasoning mechanism, deployed to select proper pieces of content—called as the learning pills—according to the learner’s requirements. In the first part of this article, we analyse the organisation of learning process and basic pieces of knowledge delivered in it. Later, we introduce an intelligent auto-adaptive information delivery mechanism. Finally, we discuss its abilities, future research and summarise the paper.

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References

  1. Beldaglia, B., Adiguzela, T.: Illustrating an ideal adaptive e-learning: a conceptual framework. Procedia Soc. Behav. Sci. 2, 5755–5761 (2010)

    Article  Google Scholar 

  2. Brusilovsky, P.: Adaptive and intelligent technologies for web-based education. Spec. Issue Intell. Syst. Teleteaching 4, 19–25 (1999)

    Google Scholar 

  3. dall’Acqua, L.: A model for an adaptive e-learning environment. In: Proceedings of the World Congress on Engineering and Computer Science, WCECS 2009, vol. 1, pp. 604–609 (2009)

    Google Scholar 

  4. Dunlap, J., Dobrovolny, J., Young, D.: Preparing e-learning designers using kolb’s model of experiential learning. Innov.: J. Online Educ. (2008)

    Google Scholar 

  5. Dżega, D., Pietruszkiewicz, W.: Intelligent Decision-Making Support within the E-Learning Process, pp. 497–521. Springer (2012)

    Google Scholar 

  6. Kolb, A., Kolb, D.A.: Experiential Learning Theory Bibliography 1971–2001 (2001)

    Google Scholar 

  7. Moebs, S.: A learner, is a learner, is a user, is a customer: Qos-based experience-aware adaptation. In: Proceedings of the 16th ACM international conference on Multimedia, pp. 1035–1038 (2008)

    Google Scholar 

  8. Paramythis, A., Loidl-Reisinger, S.: Adaptive learning environments and e-learning standards. In: 2nd European Conference on e-Learning (ECEL 2003), (November 2003)

    Google Scholar 

  9. Pietruszkiewicz, W., Dżega, D.: The Artificial Intelligence in the Support of e-Learning Management and Quality Maintenance. IGI Global, pp. 92–127 (2012)

    Google Scholar 

  10. Pietruszkiewicz, W., Dżega, D.: Auto-adaptive web e-content presentation. In: Skulimowski, A.M.J. (ed.) Proceedings of the 8th International Conference on Knowledge, Information, and Creativity Support Systems (KICSS 2013), pp. 531–540 (November 2013)

    Google Scholar 

  11. Pushpa, M.: ACO in e-learning: towards an adaptive learning path. Int. J. Comput. Sci. Eng. 4(3), 458–462 (2012)

    Google Scholar 

  12. Stoyanov, S., Kirschner, P.: Expert concept mapping method for defining the characteristics of adaptive e-learning: Alfanet project case. Educ. Tech., Res. Dev. 52(2), 41–56 (2004)

    Article  Google Scholar 

  13. Vasilyeva, E., Pechenizkiy, M., Bra, P.: Adaptation of feedback in e-learning system at individual and group level. In: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 235–244 (2008)

    Google Scholar 

  14. Wiggins, G., Trewin, S.: A system for concerned teaching of musical aural skills. In: Gauthier, G., Frasson, C., VanLehn, K. (eds.) Intelligent Tutoring Systems. Lecture Notes in Computer Science, vol. 1839, pp. 494–503. Springer, Berlin (2000)

    Google Scholar 

  15. Woźniak, P.: Algorithm used in supermemo 8 for windows. http://www.supermemo.com/english/algsm8.htm. Accessed: (September 2013)

  16. Woźniak, P.: Repetition spacing algorithm used in supermemo 2002 through supermemo 2006. http://www.supermemo.com/english/algsm11.htm. Accessed: (September 2013)

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Acknowledgments

This research was funded by the research grant N115 413240 from the National Science Centre in Poland.

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Correspondence to Dorota Dżega .

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Pietruszkiewicz, W., Dżega, D. (2016). Information Adaptation by an Intelligent Knowledge-Oriented Mechanism. In: Skulimowski, A., Kacprzyk, J. (eds) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing, vol 364. Springer, Cham. https://doi.org/10.1007/978-3-319-19090-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-19090-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19089-1

  • Online ISBN: 978-3-319-19090-7

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