In recent years, a number of systems have been developed to detect differences in how students choose to use intelligent tutoring systems, and the attitudes and goals which underlie these decisions. These systems, when trained using data from human observations and questionnaires, can detect specific behaviors and attitudes with high accuracy. However, such data is time-consuming to collect, especially across an entire tutor curriculum. Therefore, to deploy a detector of behaviors or attitudes across an entire tutor curriculum, the detector must be able to transfer to a new tutor lesson without being re-trained using data from that lesson. In this paper, we present evidence that detectors of gaming the system can transfer to new lessons without re-training, and that training detectors with data from multiple lessons improves generalization, beyond just the gains from training with additional data. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Baker, R. S. J. D., Corbett, A. T., Koedinger, K. R., & Roll, I. (2006). Generalizing detection of gaming the system across a tutoring curriculum. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4053 LNCS, pp. 402–411). Springer Verlag. https://doi.org/10.1007/11774303_40
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