Difficulty-Controllable Reading Comprehension Question Generation Considering the Difficulty of Reading Passages
DOI:
https://doi.org/10.58459/icce.2024.4931Abstract
In recent years, various question generation (QG) methods for reading comprehension have been that automatically generate questions related to given reading passages. Specifically, QG methods based on deep neural networks have succeeded in generating high-quality questions. To apply such QG methods in educational systems, such as intelligent tutoring systems and adaptive learning systems, it is crucial to generate questions with difficulty levels that are appropriate for each learner's reading ability. To meet this need, several difficulty-controllable QG have been proposed recently. However, a limitation of existing difficulty- controllable methods is that they overlook the difficulty of the reading passages, which are given as the input context for QG. Since the difficulty of reading passages can affect the difficulty of the generated questions, selecting reading passages with appropriate difficulty is crucial. Therefore, in this study, we develop a difficulty-controllable QG that includes a mechanism for selecting reading passages with appropriate difficulty for each learner. Our approach begins with the of a new item response theory (IRT) model capable of simultaneously estimating the difficulty of both questions and reading passages. Using the developed IRT model and the latest IRT- based difficulty-controllable QG method, we propose a framework to select reading passages and generate questions that are appropriate for each learner's reading ability.