基于块的学习：一种多智能体辩论辅助的个性化自适应语言学习框架
Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning
AI总结：
本文提出基于块的学习框架，利用多智能体辩论技术评估语言能力，结合评分和推荐机制，通过间隔复习和掌握进度提升语言学习效果。
摘要：
大多数数字语言学习课程依赖于离散项目测验，仅测试记忆而非实际交流能力。当进度由测验成绩驱动时，学习者可能在互动中仍存在语法和词汇的不足。近期基于大语言模型的评分工作表明，通过评分开放性对话可实现进步，但需要可靠的评分协议。我们引入Learning in Blocks框架，通过CEFR对齐的评分标准评估对话能力。该框架采用异构多智能体辩论（HeteroMAD）分两阶段进行：评分阶段中，角色专业化代理独立评估语法、词汇和互动交流，进行辩论解决冲突判断，然后由法官综合得出共识评分；推荐阶段识别特定语法技能和词汇主题进行针对性复习。进度要求达到70%的掌握率，间隔复习针对弱点以防止技能退化。我们对四种评分和推荐方法在CEFR A2对话上进行基准测试，HeteroMAD在评分一致性上达到0.23的变异度，推荐接受度为90.91%。一项为期8周的180名CEFR A2学习者研究显示，结合评分和推荐机制与间隔复习和掌握进度的学习效果优于仅反馈。
英文摘要：
Most digital language learning curricula rely on discrete-item quizzes that test recall rather than applied conversational proficiency. When progression is driven by quiz performance, learners can advance despite persistent gaps in using grammar and vocabulary during interaction. Recent work on LLM-based judging suggests a path toward scoring open-ended conversations, but using interaction evidence to drive progression and review requires scoring protocols that are reliable and validated. We introduce Learning in Blocks, a framework that grounds progression in demonstrated conversational competence evaluated using CEFR-aligned rubrics. The framework employs heterogeneous multi-agent debate (HeteroMAD) in two stages: a scoring stage where role-specialized agents independently evaluate Grammar, Vocabulary, and Interactive Communication, engage in debate to address conflicting judgments, and a judge synthesizes consensus scores; and a recommendation stage that identifies specific grammar skills and vocabulary topics for targeted review. Progression requires demonstrating 70% mastery, and spaced review targets identified weaknesses to counter skill decay. We benchmark four scoring and recommendation methods on CEFR A2 conversations annotated by ESL experts. HeteroMAD achieves a superior score agreement with a 0.23 degree of variation and recommendation acceptability of 90.91%. An 8-week study with 180 CEFR A2 learners demonstrates that combining rubric-aligned scoring and recommendation with spaced review and mastery-based progression produces better learning outcomes than feedback alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
