用视觉模型自动评分学生科学绘图，还能判断何时该人工复核。
Confidence-Aware Automated Assessment of Student-Drawn Scientific Models
- 基于视觉变换器的模型，通过自适应学习识别学生绘图
- 在6个初中科学题上实现高可靠评分，自动覆盖率达85%以上
- 引入置信度机制，降低误判风险，适合教育评估场景
学生绘制的科学模型广泛用于依据下一代科学标准（NGSS）的建模任务中，以评估其概念理解。然而，这类绘图的评分依赖专家的人工判断来解读复杂的视觉表达，导致大规模评估在课堂环境中成本高昂且难持续。本文研究基于视觉模型的自动化评分方法，采用经过参数高效适配的视觉变换器（ViT），并提出一种置信度感知评分框架，从测试时的预测分布中提取响应级置信度。该置信度信号支持选择性自动化：对高置信度结果自动评分，对不确定案例则移交人工审查。在六个符合NGSS标准的初中评估题目上的实验表明，该方法在提升评分可靠性的同时，实现了自动化覆盖率与评分风险之间的实用权衡，凸显了置信度感知方法在可信教育评估中的价值。
原文摘要 · Abstract (English)
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.
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