2018/08/29 by Yi-Ting Huang, Huang, Yi-Ting, Meng Chang Chen +3
Computer Science · #Artificial Intelligence (cs.AI) #Educational Technology and Assessment #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Intelligent Tutoring Systems and Adaptive Learning #Topic Modeling #cs.AI #cs.HC
paper · pdf · doi:10.48550/arxiv.1808.09735
arxiv created 2018/08/29 · openalex publication_date 2018/08/29 · arxiv updated 2018/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.