A Multi-dimensional Taxonomy for Automating Hinting

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, 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 five dimensions: (1) domain knowledge reference, (2) inferential role, (3) elicitation status, (4) discourse dynamics, and (5) problem solving perspective. These fine-grained distinctions support the constructive generation of hint specifications from modular knowledge sources.

• Type: Conference
• Year: 2004
• Where: ITS
• Authors: Dimitra Tsovaltzi, Armin Fiedler, Helmut Horacek
• Keywords: Artificial Intelligence, Hints, ITS 2004, Multi-dimensional Hint Taxonomy, Problem Solving