Abstract
We present results from an analysis of students’ shallow behaviors, i.e., gaming, during their interaction with an Intelligent Tutoring System (ITS). The analysis is based on six college classes using the Andes ITS for homework and test preparation. Our findings show that student features are a better predictor of gaming than problem features, and that individual differences between students impact where and how students game.
Keywords
- Student Feature
- Intelligent Tutoring System
- Educational Data Mining
- Gaming Behavior
- Performance Goal Orientation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Muldner, K., Burleson, W., Van de Sande, B., VanLehn, K. (2010). An Analysis of Gaming Behaviors in an Intelligent Tutoring System. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13388-6_23
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DOI: https://doi.org/10.1007/978-3-642-13388-6_23
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