Bringing personalized learning into computer-aided question generation
Abstract
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.
Cite
@article{arxiv.1808.09735,
title = {Bringing personalized learning into computer-aided question generation},
author = {Yi-Ting Huang and Meng Chang Chen and Yeali S. Sun},
journal= {arXiv preprint arXiv:1808.09735},
year = {2018}
}