这篇论文教你用ViT给学生的科学画图自动打分，还能判断哪些该机器批、哪些该人看，很适合做教育评估的参考。
研究提出基于Vision Transformer（ViT）的自信度感知评分框架，用于自动评估学生绘制的科学模型。在6个NGSS对齐的中学评估项目上，该方法通过从测试时预测分布中提取响应级自信度，实现高置信度响应自动评分，低置信度响应转人工审核。相比传统方法，该框架在保持评分可靠性的同时，支持自动覆盖率和评分风险之间的实用权衡。
Confidence-Aware Automated Assessment of Student-Drawn Scientific Models
Student-generated drawings are widely used in science education to assess learners' conceptual understanding in modeling-based tasks aligned with the Next Generation Science Standards (NGSS). However, scoring such drawings requires expert human judgment to interpret complex visual representations, making large-scale assessment costly to implement and sustain in classroom settings. In this work, we study automated scoring of student-generated scientific drawings using a vision-based model. We evaluate a Vision Transformer (ViT) with parameter-efficient adaptation and propose a confidence-aware scoring framework that derives response-level confidence from test-time predictive distributions. This confidence signal enables selective automation by scoring high-confidence responses automatically while deferring uncertain cases for human review. Experiments on six NGSS-aligned middle school assessment items show that the proposed approach improves scoring reliability while supporting a practical trade-off between automated coverage and scoring risk, highlighting the value of confidence-aware methods for trustworthy educational assessment.