{"ID":2881495,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16638","arxiv_id":"2508.16638","title":"Empirical Analysis of the Effect of Context in the Task of Automated Essay Scoring in Transformer-Based Models","abstract":"Automated Essay Scoring (AES) has emerged to prominence in response to the growing demand for educational automation. Providing an objective and cost-effective solution, AES standardises the assessment of extended responses. Although substantial research has been conducted in this domain, recent investigations reveal that alternative deep-learning architectures outperform transformer-based models. Despite the successful dominance in the performance of the transformer architectures across various other tasks, this discrepancy has prompted a need to enrich transformer-based AES models through contextual enrichment. This study delves into diverse contextual factors using the ASAP-AES dataset, analysing their impact on transformer-based model performance. Our most effective model, augmented with multiple contextual dimensions, achieves a mean Quadratic Weighted Kappa score of 0.823 across the entire essay dataset and 0.8697 when trained on individual essay sets. Evidently surpassing prior transformer-based models, this augmented approach only underperforms relative to the state-of-the-art deep learning model trained essay-set-wise by an average of 3.83\\% while exhibiting superior performance in three of the eight sets. Importantly, this enhancement is orthogonal to architecture-based advancements and seamlessly adaptable to any AES model. Consequently, this contextual augmentation methodology presents a versatile technique for refining AES capabilities, contributing to automated grading and evaluation evolution in educational settings.","short_abstract":"Automated Essay Scoring (AES) has emerged to prominence in response to the growing demand for educational automation. Providing an objective and cost-effective solution, AES standardises the assessment of extended responses. Although substantial research has been conducted in this domain, recent investigations reveal t...","url_abs":"https://arxiv.org/abs/2508.16638","url_pdf":"https://arxiv.org/pdf/2508.16638v2","authors":"[\"Abhirup Chakravarty\"]","published":"2025-08-17T17:17:34Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.CL\"]","methods":"[\"Transformer\"]","has_code":false}