Personalized Automatic Quiz Generation Based on Proficiency Level Estimation

Abstract

Recent years have seen increased attention given to computer-aided question generation for language student testing and evaluation. However, this approach often directly provides examinees with exhaustive questions. This is inappropriate, because these questions are not designed for any specific testing purpose. In this work, we present a personalized automatic quiz generation model that generates multiple-choice questions at various difficulty levels and categories, including grammar, vocabulary, and reading comprehension. We combined this model with a quiz strategy for estimating examinee proficiency and question selection. The proficiency is estimated using Exponential Moving Average, combining the test responses with a student’s past history. The results show that the subjects in the experimental group corrected their mistakes more frequently as well as answered more difficult questions than the control group. The experimental group also demonstrated the most progress between the pre-test and post-test. In addition, most of subjects agree the quality of the generated questions in the questionnaire analysis.