发表机构
Bangladesh Army University of Science and Technology (BAUST)（孟加拉国陆军科技大学（BAUST））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该文发布了含OBE评分元数据的多模态考试答案数据集，可支持自动评估、多模态文档理解等研究，访问仅限科研用途。
AI 中文摘要
本文是一篇数据文章，介绍了一个多模态考试答案数据集，包含扫描的考试答案及专家设计的成果导向教育（Outcome-Based Education，OBE）评分元数据。该数据集包含来自4所学术机构的415名同意参与的学生提交的485份答案答卷，8名教师贡献了涵盖9个学科、12种不同题目模板的考试材料。每个答案条目将扫描的PDF文件与随机标识符、学科标签、题目、参考答案、准则定义、表现水平描述、准则分数及总分相关联。12套评分规则总计包含47项准则。扫描文件保留了真实的学术内容，包括手写文字、印刷文本、公式、表格、代码、图表、草图和示意图。扫描来源包括CamScanner、Adobe Scan及常规扫描仪，带来了光照、对比度、方向、压缩率和分辨率的差异；多样的手写笔迹、划掉的内容、修改的计算过程及插入的修正，进一步增加了视觉变异性，可用于鲁棒性和泛化性研究。数据准备涉及异构源整合、标签和文本标准化、分数验证、标识符随机化、文件名随机化及JSON转PDF的完整性检查。答案级审核确认存在485个唯一标识符、485个唯一PDF文件名，每个总分与准则分数之和一致，且分数在适用评分规则的最大值范围内。该数据可支持感知评分规则的自动评估、多模态文档理解、准则级反馈、分数预测及隐私感知OBE评估研究，访问权限仅限研究使用，可在合理要求下从通讯作者处获取。
英文摘要
This data article describes a multimodal collection of scanned examination answers paired with expert-designed Outcome-Based Education (OBE) grading metadata. The collection contains 485 answer submissions from 415 consenting students at four academic institutions. Eight faculty contributors supplied examination materials covering nine subjects and 12 distinct question templates. Each answer-level item links a scanned PDF to a randomized identifier, subject label, question, model answer, criterion definitions, performance-level descriptions, criterion marks, and a total mark. The 12 rubrics contain 47 criteria in total. The scans retain realistic academic content, including handwriting, printed text, equations, tables, code, figures, sketches, and diagrams. CamScanner, Adobe Scan, and conventional scanners contributed variation in illumination, contrast, orientation, compression, and resolution. Diverse handwriting, crossed-out work, revised calculations, and inserted corrections add further visual variability for robustness and generalization studies. Preparation involved heterogeneous-source consolidation, label and text standardization, score validation, identifier randomization, filename randomization, and JSON-to-PDF integrity checks. An answer-level audit confirmed 485 unique identifiers, 485 unique PDF filenames, agreement between each total mark and its criterion-mark sum, and scores within the applicable rubric maximum. The data can support rubric-aware automated evaluation, multimodal document understanding, criterion-level feedback, score prediction, and privacy-aware OBE assessment research. Access is restricted to research use and is available from the corresponding author upon reasonable request.
