Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
Mind Bugs: The Origins of Procedural Misconceptions
Mind Bugs: The Origins of Procedural Misconceptions
Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Affect-aware tutors: recognising and responding to student affect
International Journal of Learning Technology
Generating task-specific next-step hints using domain-independent structures
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
An authoring language as a key to usability in a problem-solving ITS framework
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Integrating sophisticated domain-independent pedagogical behaviors in an ITS framework
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Automating the modeling of learners' erroneous behaviors in model-tracing tutors
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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ASTUS is an authoring framework designed to create model-tracing tutors with similar efforts to those needed to create Cognitive Tutors. Its knowledge representation system was designed to model the teacher's point of view of the task and to be manipulated by task independent processes such as the automatic generation of sophisticated pedagogical feedback. The first type of feedback we automated is instructions provided as next step hints. Whereas next step hints are classically authored by teachers and integrated in the model of the task, our framework automatically generates them from task independent templates. In this paper, we explain, using examples taken from a floating-point number conversion tutor, how our knowledge representation approach facilitates the generation of next-step hints. We then present experiments, conducted to validate our approach, showing that generated hints can be as efficient and appreciated as teacher authored ones.