从应试到认知支架：一种面向LLM的教育诊断基准测试标准测试
From Test-taking to Cognitive Scaffolding: A Pedagogical Diagnostic Benchmark for LLMs on English Standardized Tests
AI总结：
本文提出一种教育诊断基准，通过认知框架模拟英语标准化测试问题解决过程，展示ESTBook基准测试在识别认知轨迹和改进教学推理中的实用价值。
摘要：
随着大型语言模型（LLMs）越来越多地融入教育工具，当前对标准化测试的评估主要集中在二元结果准确性上。相反，一个有效的AI导师必须表现出忠实的推理、阐明解决方案策略并诊断特定的人类误解。为此，我们介绍了一种教育诊断框架，将英语标准化测试（EST）问题解决过程建模为通过认知框架的旅行。基于此框架，我们提出了ESTBook，一个包含10,576个问题和29种任务类型的多模态基准测试，涵盖五个主要考试。与传统数据集不同，ESTBook超越了数据聚合，通过正式化的推理轨迹和干扰项理由丰富问题，以捕捉特定的认知陷阱。通过广泛的评估，我们实证地展示了我们诊断框架的实用价值，表明识别认知轨迹有助于缩小性能差距并通过引导提取提高教学推理。
英文摘要：
As large language models (LLMs) are increasingly integrated into educational tools, current evaluations on standardized tests predominantly focus on binary outcome accuracy. Instead, an effective AI tutor must exhibit faithful reasoning, elucidate solution strategies, and diagnose specific human misconceptions. To bridge this gap, we introduce a pedagogical diagnostic framework that models English Standardized Test (EST) problem-solving as a traversal through a cognitive framework. Based on this framework, we present ESTBook, a multimodal benchmark encompassing 10,576 questions and 29 task types across five major exams. Unlike traditional datasets, ESTBook goes beyond data aggregation by enriching questions with formalized reasoning trajectories and distractor rationales that capture specific cognitive traps. Through extensive evaluations, we empirically demonstrate the practical utility of our diagnostic framework, showing that identifying cognitive trajectories facilitates the mitigation of performance gap and improves pedagogical reasoning through guided elicitation.
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。
