Extending Domain Theories: Two Case Studies in Student Modeling
Machine Learning
Unified theories of cognition
Multistrategy Learning and Theory Revision
Machine Learning - Special issue on multistrategy learning
Modeling Cognitive Development on Balance Scale Phenomena
Machine Learning - Special issue on computational models of human learning
A decision-tree model of balance scale development
Machine Learning
Procedural help in Andes: generating hints using a Bayesian network student model
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A novel application of theory refinement to student modeling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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For an effective Teacher-Student interaction, the Teacher has to maintain a constant understanding of "what is going on" in the Student's mind. When coming to Physics, the Teacher's ability to propose and to relate explanations at different levels of abstraction - as a chains of causal interactions (deep) or as a set of observable phenomena (shallow) - may determine a successful and lasting learning in the Student. Here, we describe a knowledge representation to be used by the teacher to depict to herself the student's mental model and to tune her future lessons according to the current student comprehension. Supported by a cognitive theory of children physics learning, we used the system WHY for modeling the evolution of a student's learning as it appeared at the teacher's eyes. Two of WHY's features turned out to be essential: (a) to deal with explanations having different levels of abstraction, and (b) the possibility to continuously evaluate the coherence of the hypothesized learner's model with respect to her explanation. In the long term, the work's outcome might contribute to the development of teaching assistant systems that support the teacher in identifying "what has to be explained next".