Abstract
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.
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Tsovaltzi, D., Fiedler, A., Horacek, H. (2004). A Multi-dimensional Taxonomy for Automating Hinting. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds) Intelligent Tutoring Systems. ITS 2004. Lecture Notes in Computer Science, vol 3220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30139-4_73
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DOI: https://doi.org/10.1007/978-3-540-30139-4_73
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