用半自动方式评纸质试卷，提升大班考试的公平与效率
Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations
- 手写答案转结构化数据，再由视觉大模型识别评分
- 结合双轮验证和标准答案比对，误判率显著降低
- 适合大规模考试且保留开放题优势，教师负担更轻
本文分析了高等教育总结性考试中完全数字化和部分数字化e评估方法的局限性，重点关注封闭式题型导致的教学窄化，以及在大规模学生群体中凸显的组织、技术与法律约束。为此，提出一种混合式e评估方法：保留纸质、问题导向的考试任务，同时实现半自动化评分。将评估相关中间结果以结构化格式手写填入表格，随后通过扫描捕捉。核心挑战在于真实考试条件下对手写字符的可靠识别。结合具备视觉能力的大语言模型与双轮验证机制，并对比标准答案，可有效减少误判，从而提升总结性评估的有效性、公平性与可扩展性。
原文摘要 · Abstract (English)
This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education. The analysis focuses on the didactic narrowing caused by closed question formats and on organizational, technical, and legal constraints that become particularly relevant in large student cohorts. As an alternative, the paper proposes a hybrid e-assessment approach that retains paper-based, problem-oriented examination tasks while enabling semi-automated grading. Assessment-relevant intermediate results are encoded in a structured answer format, entered by students by hand, and subsequently captured from table fields. The central technical bottleneck is reliable recognition of handwritten characters under realistic examination conditions. Recent vision-capable large language models, combined with a two-pass validation principle and comparison against a solution key, can reduce misclassifications and thereby improve the validity, fairness, and scalability of summative assessment.
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。