educational-assessment
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- PaperarXiv — AI in Education (cs.CY)15 Jul 2026
When Rubrics Change: Cross-Rubric Generalization for Critical Thinking Essay Scoring
Nischal Ashok Kumar, Payu Wittawatolarn, Sana Kang, Marisa C. Peczuh et al.
The paper investigates cross-rubric generalization in automated essay scoring, where models trained on essays scored under one rubric must perform on new rubrics targeting different aspects. Using a trait-based intermediate representation and target-essay supervision, the approach improves macro F1 by 5% in the hardest setting. Their best open-source Llama-based model outperforms GPT-5-mini prompting by 2.1% and trails GPT-5 by 1.9%.
Original abstract
Automated essay scoring (AES) research has largely focused on cross-prompt generalization, where essays from unseen prompts are scored while the scoring criteria are typically held constant. In practice, however, educators may revise or even introduce new rubrics in their scoring task, to evaluate different aspects of essays. We study cross-rubric generalization: training on essays labeled under one set of rubrics and evaluating on previously unseen rubrics, which target different aspects of the essay. We use a Large Language Model (LLM) fine-tuning framework with two components: rubric-agnostic intermediate representations, called traits, and target-essay supervision under seen rubrics during training. On an AES dataset augmented with multiple rubric-defined labels of student critical thinking skills, we find that traits improve macro F1 by 5.0% over a baseline without traits in the hardest setting, where both target rubrics and target essays are unseen during training. We further find that increasing target-essay supervision improves performance, with our best fine-tuned open-source Llama-based model outperforming GPT-5-mini prompting by 2.1% macro F1 and trailing GPT-5 by 1.9%. These results show that trait-based intermediate structure and controlled supervision improve generalization to unseen rubrics.
- PaperAssessment & Evaluation in Higher Education1 Jul 2026
Which grades predict what? A more nuanced understanding of using high school results for university admission
Sebastiaan Steenman, Ada Kool
This study examined the predictive value of high school grades for university performance across different cognitive learning objectives, programs, and time. Overall high school GPA consistently outperformed subject-specific grades, but taking related high school subjects was associated with small improvements. Predictive strength declined over the course of bachelor programs and was better for lower-order cognitive skills.
Original abstract
While high school grades are widely used for university admissions, little is known about which specific high school grades best predict what type of performance at university. This study examines the predictive value of overall high school GPA (grade point average), grades for subsets of subjects, and the added value of having taken specific subjects, for university performance across different cognitive learning objectives, different programmes and over time. Using data from multiple cohorts of six undergraduate programmes at a large Dutch research university, we show that the overall high school GPA consistently outperforms subsets of discipline-related subjects, suggesting that high school grades primarily represent general learning skills and traits. However, having taken a specific related high school subject was generally associated with better university performance, although effect sizes were small. High school grades predicted performance better on assessments targeting lower-order cognitive skills than complex academic tasks. No significant differences emerged between the predictive value of high school final-year and penultimate-year grades. Finally, the predictive strength declined over the course of the three-year bachelor programmes. These findings highlight the need for careful consideration of which high school grades to use in admissions and provide practical suggestions for university admissions officers to do so.