Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Evaluating tutors that listen: an overview of project LISTEN
Smart machines in education
Some useful tactics to modify, map and mine data from intelligent tutors
Natural Language Engineering
Identifiability: A Fundamental Problem of Student Modeling
UM '07 Proceedings of the 11th international conference on User Modeling
What Level of Tutor Interaction is Best?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A bayes net toolkit for student modeling in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
An analysis of students' gaming behaviors in an intelligent tutoring system: predictors and impacts
User Modeling and User-Adapted Interaction
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Researchers have used various methods to evaluate the fine-grained interactions of intelligent tutors with their students. We present a case study comparing three such methods on the same data set, logged by Project LISTEN's Reading Tutor from usage by 174 children in grades 2-4 (typically 7-10 years) over the course of the 2005-2006 school year. The Reading Tutor chooses randomly between two different types of reading practice. In assisted oral reading, the child reads aloud and the tutor helps. In "Word Swap," the tutor reads aloud and the child identifies misread words. One method we use here to evaluate reading practice is conventional analysis of randomized controlled trials (RCTs), where the outcome is performance on the same words when encountered again later. The second method is learning decomposition, which estimates the impact of each practice type as a parameter in an exponential learning curve. The third method is knowledge tracing, which estimates the impact of practice as a probability in a dynamic Bayes net. The comparison shows qualitative agreement among the three methods, which is evidence for their validity.