通过图对齐自动识别学生答案中的缺失内容，助力形成性评价。
Directed Graph-alignment Approach for Identification of Gaps in Short Answers
- 将学生答案与标准答案建模为有向图，进行图对齐识别缺失项。
- 在UNT、SciEntsBank和Beetle数据集上验证，整体表现良好。
- 适用于自动评分系统，尤其适合需要精准反馈的教育场景。
本文提出一种自动识别学生答案中缺失内容（即‘缺口’）的方法，通过将学生答案与对应的标准答案建模为有向图，并进行图对齐实现。缺口可在词、短语或句子层面被识别，有助于为学生提供形成性评估反馈。为验证该方法，构建了包含三个主流短答案评分数据集（UNT、SciEntsBank、Beetle）的标注缺口数据集，已公开于https://github.com/sahuarchana7/gaps-answers-dataset。采用传统机器学习任务评估指标对缺口识别任务进行评测，结果显示该方法在不同数据集和答案类型上表现有所差异，但整体效果令人鼓舞。
原文摘要 · Abstract (English)
In this paper, we have presented a method for identifying missing items known as gaps in the student answers by comparing them against the corresponding model answer/reference answers, automatically. The gaps can be identified at word, phrase or sentence level. The identified gaps are useful in providing feedback to the students for formative assessment. The problem of gap identification has been modelled as an alignment of a pair of directed graphs representing a student answer and the corresponding model answer for a given question. To validate the proposed approach, the gap annotated student answers considering answers from three widely known datasets in the short answer grading domain, namely, University of North Texas (UNT), SciEntsBank, and Beetle have been developed and this gap annotated student answers' dataset is available at: https://github.com/sahuarchana7/gaps-answers-dataset. Evaluation metrics used in the traditional machine learning tasks have been adopted to evaluate the task of gap identification. Though performance of the proposed approach varies across the datasets and the types of the answers, overall the performance is observed to be promising.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。