A classifier to detect student ‘gaming’ of a medical education system
نویسندگان
چکیده
We report on the training, testing and application of a classifier for detecting ‘gaming the system’ – a well documented behavior in which students exploit the properties of an educational system to succeed without learning. Using outcomes based on expert judgments of user-system interaction sequences, we trained a decision tree classifier, using ten-fold cross validation. The area under the ROC curve was 0.72, significantly better than chance: Z=10.90, two-tailed p<0.001. We applied the resulting gaming detector model to 160 hours of interaction data from 70 users in four different studies. Results show that gaming behaviors are frequently observed, and that gaming frequency is negatively correlated with learning gains. The resulting gaming detector will be used in the SlideTutor system to automate gaming detection, and intervene when gaming is observed.
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