An analysis of students' gaming behaviors in an intelligent tutoring system: Predictors and impacts

Kasia Muldner, Winslow Burleson, Brett Van De Sande, Kurt VanLehn

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)99-135
Number of pages37
JournalUser Modeling and User-Adapted Interaction
Volume21
Issue number1-2
DOIs
StatePublished - Apr 1 2011

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Keywords

  • Bayesian network parameter learning
  • Educational data mining
  • Gaming
  • Utility of hints

ASJC Scopus subject areas

  • Education
  • Human-Computer Interaction
  • Computer Science Applications

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