Bringing personalized learning into computer-aided question generation
This addresses personalized learning in education, but it is incremental as it builds on existing ability estimation methods.
The paper tackles the problem of estimating student ability for personalized computer-aided question generation by proposing a statistical method based on acquisition distributions, which matches actual abilities and leads to significant improvement in test scores for the experimental group.
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.