INMA: A Knowledge-Based Authoring Tool for Music Education

  • Maria Virvou
  • Aristomenis S. Lampropoulos
  • George A. Tsihrintzis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


A knowledge-based authoring tool and its domain-knowledge acquisition capabilities are described. The authoring tool is called INMA and assists human tutors to create their own Intelligent Tutoring Systems (ITSs) for music education. Learner modeling in the resulting ITSs is primarily based on a cognitive theory that models human reasoning and is called Human Plausible Reasoning. The particular theory makes use of a very detailed knowledge representation of the domain of the part of music to be taught in the form of hierarchies, so that similarities, dissimilarities, generalizations and specializations among the tutoring concepts may be inferred. INMA incorporates a special knowledge acquisition component that can assist instructional designers on the task of constructing the music hierarchies required. The operation of this component is based on content-based retrieval and semantic meta-data.


Instructional Designer Knowledge Representation Relevance Feedback Intelligent Tutor System Authoring Tool 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boyle, T.: Design for Multimedia Learning. Prentice-Hall, Englewood Cliffs (1997)Google Scholar
  2. 2.
    Collins, A., Michalski, R.: The logic of plausible reasoning: A core theory. Cognitive Science 13, 1–49 (1989)CrossRefGoogle Scholar
  3. 3.
    Lampropoulos, A.S., Sotiropoulos, D.N., Tsihrintzis, G.A.: Individualization of Music Similarity Perception via Feature Subset Selection. In: IEEE, International Conference on Systems, Man & Cybernetics 2004, The Hague, Netherlands, October 10-13 (2004)Google Scholar
  4. 4.
    Lampropoulos, A.S., Tsihrintzis, G.A.: Semantically Meaningful Music Retrieval with Content-Based Features and Fuzzy Clustering. In: 5th International Workshop on Image Analysis for Multimedia Interactive Services, Lisbon, Portugal (2004)Google Scholar
  5. 5.
    Lu, Y., et al.: A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems. In: Proceedings of ACM MULTIMEDIA 2000, Los Angeles California, pp. 31–38 (2000)Google Scholar
  6. 6.
    Murray, T.: Authoring intelligent tutoring systems: an analysis of the state of the art. International Journal of Artificial Intelligence in Education 10, 98–129 (1999)Google Scholar
  7. 7.
    Self, J.: The defining characteristics of Intelligent Tutoring Systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education 10, 350–364 (1999)Google Scholar
  8. 8.
    Stansfield, J.C., Carr, B., Goldstein, I.P.: Wumpus advisor I: a first implementation of a program that tutors logical and probabilistic reasoning skills. At Lab Memo 381. Massachusetts Institute of Technology, Cambridge, Massachusetts (1976)Google Scholar
  9. 9.
    Tzanetakis, G., Cook, P.: Musical Genre Classification of Audio Signals. IEEE Transactions on Speech and Audio Processing 10(5) (July 2002)Google Scholar
  10. 10.
    Virvou, M.: A Cognitive Theory in an Authoring Tool for Intelligent Tutoring Systems. In: IEEE International Conference on Systems Man and Cybernetics 2002 (SMC 2002), Hammamet, Tunisia, October 2002, vol. 2, pp. 410–415 (2002)Google Scholar
  11. 11.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maria Virvou
    • 1
  • Aristomenis S. Lampropoulos
    • 1
  • George A. Tsihrintzis
    • 1
  1. 1.Department of InformaticsUniversity of PiraeusPiraeusGreece

Personalised recommendations