Predicting student help-request behavior in an intelligent tutor for reading
UM'03 Proceedings of the 9th international conference on User modeling
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Modelling Learning in an Educational Game
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Predicting student help-request behavior in an intelligent tutor for reading
UM'03 Proceedings of the 9th international conference on User modeling
Usability engineering for the adaptive web
The adaptive web
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This paper reports results on using data mining to extract useful variables from a database that contains interactions between the student and Project LISTEN's Reading Tutor. Our approach is to find variables we believe to be useful in the information logged by the tutor, and then to derive models that relate those variables to student's scores on external, paper-based tests of reading proficiency. Once the relationship between the recorded variables and the paper tests is discovered, it is possible to use information recorded by the tutor to assess the student's current level of proficiency. The major results of this work were the discovery of useful features available to the Reading Tutor that describe students, and a strong predictive model of external tests that correlates with actual test scores at 0.88.