用新框架生成多语言高阶问题，提升批判性思维训练效果
High-Order Question Generation in a Multilingual Educational Context
- 采用主张-证据-推理与发散提问框架生成问题
- 三语（巴斯克/西班牙/英语）中模型生成问题有效率超50%
- 新框架能产生结构多样问题，适合教育场景替代布卢姆框架
批判性思维是帮助学习者超越机械记忆的基础能力，而高阶提问是培养该能力的有效方式。然而，教师在实际教学中仍主要依赖低阶问题。大型语言模型在提示引导下已展现出生成高阶问题的能力，但现有研究多基于布卢姆分类法且局限于英语。本研究突破这一局限，在巴斯克语、西班牙语和英语的多语言环境下，引入主张-证据-推理（Claim-Evidence-Reasoning）与发散提问（Divergent Questioning）两种替代框架。实验表明，开源与专有模型在三种语言中均能有效生成问题，但仅约一半可答问题被教师判定为高阶。积极发现是，新框架生成的问题在结构与概念上更具多样性，具备互补潜力，可作为布卢姆分类法的可行替代。
原文摘要 · Abstract (English)
Critical thinking is a fundamental skill that helps learners move beyond simple memorization. One way to develop this skill is through high-order questioning. However, crafting such questions remains a challenge for educators, and classroom practices tend to rely on low-order questions. Large Language Models have demonstrated strong capabilities in generating high-order questions, especially when guided by prompts based on Bloom's Taxonomy. Yet, existing research has largely centered on this framework and focused only on English. This study addresses these gaps by introducing prompts grounded in two alternative frameworks: Claim-Evidence-Reasoning and Divergent Questioning within a multilingual context using Basque, Spanish, and English. Results indicate that while both an open-source and a proprietary model rather effectively generate questions in all three languages, only about half of the answerable questions are recognized by teachers as high-order. A positive finding is that the alternative frameworks produce structurally and conceptually varied questions, suggesting they could complement each other and provide viable alternatives to Bloom's Taxonomy.
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