An Improved Data-Driven Hint Selection Algorithm for Probability Tutors
نویسندگان
چکیده
Data-driven systems such as the Hint Factory have been successful at providing student guidance by extracting procedural hints from prior user data. However, when only small amounts of data are available, it may be unable to do so. We present a novel hint-selection algorithm for coherent derivational domains, such as probability, which addresses this problem by searching a frontier of viable, partially matching student states. We tested this algorithm on a dataset collected from two probability tutors and performed a cold start comparison with direct state matching. We found that our algorithm provided higher value hints to students in unknown states 55.0% of the time. For some problems, it also provided higher value hints in known states.
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