arXiv:2606. 24973v1 Announce Type: cross Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning 328 questions across five subjects and including handwritten work.
By Malachy Fox, Kavi Samra, Paul Jung
arXiv:2607. 18767v1 Announce Type: cross Abstract: The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalability.
By Lachlan McGinness
arXiv:2606. 08855v1 Announce Type: new Abstract: This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education.
By Hartwig Grabowski, Michael Canz
arXiv:2609.14284v1 Announce Type: new
Abstract: Criterion-level grading connects examination performance to learning outcomes, but manual marking introduces workload and variation between markers. Th...
By Md Khalid Syfullah, Asif Hasan Tonmoy, Saad Ahmed, S. M. Jahangir Alam
The paper introduces THPT‑Ladder, a benchmark based on Vietnam’s 2025 National High School Graduation Examination’s convex grading scheme, which rewards partial credit non‑additively. It shows that standard accuracy metrics inflate model scores because they treat partial knowledge proportionally, whereas the official rubric penalizes incomplete correct sets. Using the benchmark, the authors demonstrate that this discrepancy can shift a model’s percentile ranking by up to 13 points among over 480,000 candidates.
By Nguyen Quoc Hung, Nguyen Dang Minh, Le Nhu Quynh, Tran Khanh Linh, Nguyen Kieu Linh
The study evaluates large language model (LLM) graders on two computer‑science exams, testing 171 configurations of closed‑ and open‑weights models. While the best LLM configuration achieved a mean absolute error of 1.64/35—better than the 2.61/35 error between two human graders—its performance was highly sensitive to the prompt. A short "strict grader" preamble caused most open‑weight models to exceed acceptable error thresholds or stop grading entirely, whereas fine‑tuning with a single LoRA adapter restored parity with human graders and reduced sensitivity to harsh prompts.
By Ali Habibullah, Yazan Alshoibi, Mohammad Alshiekh, Salman Khan, Naeemullah Khan