Bringing personalized learning into computer-aided question generation
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This paper proposes a novel and statistical method of ability estimation based on acquisition distribution for a personalized computer aided question generation. This method captures the learning outcomes over time and provides a flexible measurement based on the acquisition distributions instead of precalibration. Compared to the previous studies, the proposed method is robust, especially when an ability of a student is unknown. The results from the empirical data show that the estimated abilities match the actual abilities of learners, and the pretest and post-test of the experimental group show significant improvement. These results suggest that this method can serves as the ability estimation for a personalized computer-aided testing environment.
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