根据学生兴趣生成个性化英语阅读测试，提升理解力与学习动机。
One-Topic-Doesn't-Fit-All: Transcreating Reading Comprehension Test for Personalized Learning
- 用GPT-4o重构原题库内容，匹配学生兴趣但保持语言难度一致。
- 实验显示个性化材料使理解和动机均显著提升。
- 适合英语教学、自适应学习系统研究者参考。
个性化学习在英语作为外语（EFL）教育中日益受到关注，其中参与度和动机对阅读理解至关重要。本文提出一种新方法，生成基于学生兴趣的个性化英语阅读理解测试。我们利用OpenAI的gpt-4o构建结构化内容转创流程，以RACE-C数据集为基础，生成语言特征相似但语义契合个体学习者兴趣的新篇章及选择题。方法融合主题提取、基于布卢姆分类法的问题分类、语言特征分析与内容转创，以增强学生参与感。我们在韩国开展控制实验，评估兴趣对齐材料对学生理解与动机的影响。结果表明，使用个性化阅读材料的学生在理解能力与动机维持方面显著优于使用非个性化材料者。
原文摘要 · Abstract (English)
Personalized learning has gained attention in English as a Foreign Language (EFL) education, where engagement and motivation play crucial roles in reading comprehension. We propose a novel approach to generating personalized English reading comprehension tests tailored to students' interests. We develop a structured content transcreation pipeline using OpenAI's gpt-4o, where we start with the RACE-C dataset, and generate new passages and multiple-choice reading comprehension questions that are linguistically similar to the original passages but semantically aligned with individual learners' interests. Our methodology integrates topic extraction, question classification based on Bloom's taxonomy, linguistic feature analysis, and content transcreation to enhance student engagement. We conduct a controlled experiment with EFL learners in South Korea to examine the impact of interest-aligned reading materials on comprehension and motivation. Our results show students learning with personalized reading passages demonstrate improved comprehension and motivation retention compared to those learning with non-personalized materials.
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