A Multi-dimensional Taxonomy for Automating Hinting

  • Dimitra Tsovaltzi
  • Armin Fiedler
  • Helmut Horacek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3220)


Hints are an important ingredient of natural language tutorial dialogues. Existing models of hints, however, are limited in capturing their various underlying functions, since hints are typically treated as a unit directly associated with some problem solving script or discourse situation. Putting emphasis on making cognitive functions of hints explicit and allowing for automatic incorporation in a natural dialogue context, we present a multi-dimensional hint taxonomy where each dimension defines a decision point for the associated function. Hint categories are then conceived as convergent points of the dimensions. So far, we have elaborated four dimensions: (1) domain knowledge, (2) inferential role, (3) elicitation status, (4) problem referential perspective. These fine-grained distinctions support the constructive generation of hint specifications from modular knowledge sources.


Cognitive Load Inference Rule Teaching Model Relevant Concept Tutoring System 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dimitra Tsovaltzi
    • 1
  • Armin Fiedler
    • 1
  • Helmut Horacek
    • 1
  1. 1.Department of Computer ScienceSaarland UniversitySaarbrückenGermany

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