期刊
USER MODELING AND USER-ADAPTED INTERACTION
卷 21, 期 1-2, 页码 99-135出版社
SPRINGER
DOI: 10.1007/s11257-010-9086-0
关键词
Educational data mining; Gaming; Utility of hints; Bayesian network parameter learning
资金
- National Science Foundation [0705883, DRL-0910221]
- Pittsburgh Science of Learning Center [SBE-0836012]
- Direct For Education and Human Resources
- Division Of Research On Learning [0910221] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0705883] Funding Source: National Science Foundation
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.
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