Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Identifiability: A Fundamental Problem of Student Modeling
UM '07 Proceedings of the 11th international conference on User Modeling
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate?
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
A bayes net toolkit for student modeling in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Detection and analysis of off-task gaming behavior in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Analyzing student gaming with bayesian networks
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
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One of the common expectations of ITS designers is that students efficiently learn from every practice opportunity. However, when students are using an Intelligent Tutoring System, they can exhibit a variety of behaviors, such as “gaming,” which can strongly reduce learning. In this paper, we present a new approach to infer the impact of gaming on learning at the fine-grained level. We integrated a knowledge tracing model of the student's knowledge with the student's gaming state (as identified by our gaming detector). We found that when gaming, students learn almost nothing (on the order of one-twelfth to one-fiftieth as efficiently). A student's gaming amount is associated with aggregate effects on his knowledge and learning, leading to less learning even in the practice opportunities where no gaming occurs. In addition, we found that students tend to game in those skills on which they have relatively low knowledge. Furthermore, we found that knowing the identity of the studentis more important than knowing the skill for predicting whether gaming will occur.