# Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations

arXiv: https://arxiv.org/abs/2606.08855 · HTML: https://mlsanity.com/paper/2606.08855/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 7 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [An Introduction to Vision-Language Modeling](https://mlsanity.com/paper/2405.17247/) (2024)

## Built on by (most cited first, 1 of 1)

- [Towards Fully Automated Exam Grading: Fairness-Aware Recognition of Handwritten Answers with Foundation Models](https://mlsanity.com/paper/2606.11477/) (2026)

## Abstract (arXiv)

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.