A model for reasoning about persistence and causation
Computational Intelligence
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
A Case Study Empirical Comparison of Three Methods to Evaluate Tutorial Behaviors
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
International Journal of Artificial Intelligence in Education
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor
International Journal of Artificial Intelligence in Education
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
Do Performance Goals Lead Students to Game the System?
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Effects of Dissuading Unnecessary Help Requests While Providing Proactive Help
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Repairing Disengagement With Non-Invasive Interventions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Accelerated Future Learning via Explicit Instruction of a Problem Solving Strategy
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Feedback Specificity and the Learning of Intercultural Communication Skills
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
To Tutor or Not to Tutor: That is the Question
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Educational Software Features that Encourage and Discourage “Gaming the System”
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
International Journal of Artificial Intelligence in Education
User Modeling and User-Adapted Interaction
Using similarity to infer meta-cognitive behaviors during analogical problem solving
UM'05 Proceedings of the 10th international conference on User Modeling
Inducing effective pedagogical strategies using learning context features
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
An analysis of gaming behaviors in an intelligent tutoring system
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
How adaptive is an expert human tutor?
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
A bayes net toolkit for student modeling in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on 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
Adapting to when students game an intelligent tutoring system
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Generalizing detection of gaming the system across a tutoring curriculum
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Prevention of off-task gaming behavior in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Understanding tuberculosis epidemiology using structured statistical models
Artificial Intelligence in Medicine
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
Evidence conflict analysis approach to obtain an optimal feature set for bayesian tutoring systems
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
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Students who exploit properties of an instructional system to make progress while avoiding learning are said to be "gaming" the system. In order to investigate what causes gaming and how it impacts students, we analyzed log data from two Intelligent Tutoring Systems (ITS). The primary analyses focused on six college physics classes using the Andes ITS for homework and test preparation, starting with the research question: What is a better predictor of gaming, problem or student? To address this question, we developed a computational gaming detector for automatically labeling the Andes data, and applied several data mining techniques, including machine learning of Bayesian network parameters. Contrary to some prior findings, the analyses indicated that student was a better predictor of gaming than problem. This result was surprising, so we tested and confirmed it with log data from a second ITS (the Algebra Cognitive Tutor) and population (high school students). Given that student was more predictive of gaming than problem, subsequent analyses focused on how students gamed and in turn benefited (or not) from instructional features of the environment, as well as how gaming in general influenced problem solving and learning outcomes.