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- W2078272823 abstract "Examining student interactions in multi-step problems from Intelligent Tutoring Systems currently involves examining thousands of interactions from hundreds of students. We designed and implemented a sequential pattern mining algorithm and a sequence rating algorithm that together recognize interesting student action sequences and display them to the user in the context of the larger graph system. With the added feature of our algorithms in the InVis system, we hope to allow teachers and tutoring system designers to better understand student action patterns and thus cater better to their learning." @default.
- W2078272823 created "2016-06-24" @default.
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- W2078272823 date "2013-03-06" @default.
- W2078272823 modified "2023-10-17" @default.
- W2078272823 title "Using sequential pattern mining to increase graph comprehension in intelligent tutoring system student data (abstract only)" @default.
- W2078272823 cites W2136208491 @default.
- W2078272823 doi "https://doi.org/10.1145/2445196.2445416" @default.
- W2078272823 hasPublicationYear "2013" @default.
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