Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes
Yi-Ting Huang, Meng Chang Chen, Yeali S. Sun

TL;DR
This paper introduces a personalized computer-aided question generation system for English learners that adapts question difficulty and type based on individual proficiency, leading to improved correction of mistakes and learning progress.
Contribution
It presents a novel personalized question generation method that adjusts question difficulty and type according to learner proficiency and misconceptions.
Findings
Students corrected mistakes more frequently with personalized questions.
Significant progress observed between pretest and posttest.
Students answered more difficult questions correctly after personalization.
Abstract
In the last several years, the field of computer assisted language learning has increasingly focused on computer aided question generation. However, this approach often provides test takers with an exhaustive amount of questions that are not designed for any specific testing purpose. In this work, we present a personalized computer aided question generation that generates multiple choice questions at various difficulty levels and types, including vocabulary, grammar and reading comprehension. In order to improve the weaknesses of test takers, it selects questions depending on an estimated proficiency level and unclear concepts behind incorrect responses. This results show that the students with the personalized automatic quiz generation corrected their mistakes more frequently than ones only with computer aided question generation. Moreover, students demonstrated the most progress…
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Taxonomy
TopicsEducational Technology and Assessment · Natural Language Processing Techniques · Speech and dialogue systems
