TY - JOUR
T1 - An analysis of students' gaming behaviors in an intelligent tutoring system
T2 - Predictors and impacts
AU - Muldner, Kasia
AU - Burleson, Winslow
AU - Van De Sande, Brett
AU - VanLehn, Kurt
N1 - Funding Information:
Acknowledgements The authors thank the anonymous reviewers for their helpful suggestions. This research was funded the National Science Foundation, including the following grants: (1) IIS/HCC Affective Learning Companions: Modeling and supporting emotion during learning(#0705883); (2) Deeper Modeling via Affective Meta-tutoring(DRL-0910221) and (3) Pittsburgh Science of Learning Center(SBE-0836012).
PY - 2011/4
Y1 - 2011/4
N2 - 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.
AB - 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.
KW - Bayesian network parameter learning
KW - Educational data mining
KW - Gaming
KW - Utility of hints
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U2 - 10.1007/s11257-010-9086-0
DO - 10.1007/s11257-010-9086-0
M3 - Article
AN - SCOPUS:79955795644
SN - 0924-1868
VL - 21
SP - 99
EP - 135
JO - User Modeling and User-Adapted Interaction
JF - User Modeling and User-Adapted Interaction
IS - 1-2
ER -