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Educational Concept Maps for Personalized Learning Path Generation

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AI*IA 2016 Advances in Artificial Intelligence (AI*IA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10037))

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Abstract

The paper focuses on the delivery process of learning materials of a course for students by means of Educational Concept Maps (ECM), while in previous works, we presented the ECM model and its implementation, ENCODE system, as a tool to assist the teacher during the process of instructional design of a course. An ECM is composed of concepts and educational relationships, where a concept represents a learning argument, its prerequisites, learning outcomes and associated learning materials. We propose the learning materials generation founded on the ECM with suggested learning path for accessing educational resources personalized on the base of the student’s knowledge. The personalized document creation is based on a self-evaluation process of his/her knowledge and learning objectives, by pruning concepts on the original ECM and verifying for propaedeutic inconsistency. An algorithm that linearize the map generates the suggested learning path for the student.

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Authors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy

    Giovanni Adorni & Frosina Koceva

Authors
  1. Giovanni Adorni
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  2. Frosina Koceva
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Corresponding authors

Correspondence to Giovanni Adorni or Frosina Koceva .

Editor information

Editors and Affiliations

  1. University of Genoa , Genova, Italy

    Giovanni Adorni

  2. University of Parma , Parma, Italy

    Stefano Cagnoni

  3. University of Siena , Siena, Italy

    Marco Gori

  4. University of Genova , Genova, Italy

    Marco Maratea

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Adorni, G., Koceva, F. (2016). Educational Concept Maps for Personalized Learning Path Generation. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-49130-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49129-5

  • Online ISBN: 978-3-319-49130-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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