Vision-Language Models for Criterion-Level Grading of Handwritten Examinations in Outcome-Based Education
Criterion-level grading connects examination performance to learning outcomes, but manual marking introduces workload and variation between markers. This study evaluates vision-language models (VLMs) for handwritten outcome-based assessment across five dimensions: accuracy, human agreement, repeated-run reliability, error concentration, and explanation quality. Using 1,982 criterion-level records from 485 undergraduate examination answers, we compare 20 configurations spanning Qwen2.5-VL, InternVL3, Pixtral, a Donut baseline, and a cascade ensemble. Evaluation setups include zero-shot prompting, few-shot prompting, partial fine-tuning, and Low-Rank Adaptation (LoRA). Two independent facul...
| Authors | Asif Hasan Tonmoy, Saad Ahmed, Md Khalid Syfullah et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 September 2026 |
| Last updated at source | 15 September 2026 |
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