Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes
원문 초록
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 between the pretest and post test and correctly answered more difficult questions. Finally, we investigated the personalizing strategy and found that a student could make a significant progress if the proposed system offered the vocabulary questions at the same level of his or her proficiency level, and if the grammar and reading comprehension questions were at a level lower than his or her proficiency level.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractors
Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights
Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework
Bench4KE: Benchmarking Automated Competency Question Generation
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
Multi-Hop Question Generation via Dual-Perspective Keyword Guidance
토론
이 논문과 연결된 포럼 글이 아직 없습니다.