  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.
