Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models
In the field of automated essay scoring (AES), the task of evaluating written compositions has been a persistent challenge. Despite the impressive capabilities of generalized transformer models in various natural language processing (NLP) domains, their application to essay scoring has often fallen...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843186/ |
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| author | Tahira Amin Zahoor-Ur-Rehman Tanoli Farhan Aadil Khalid Mahmood Awan Sangsoon Lim |
| author_facet | Tahira Amin Zahoor-Ur-Rehman Tanoli Farhan Aadil Khalid Mahmood Awan Sangsoon Lim |
| author_sort | Tahira Amin |
| collection | DOAJ |
| description | In the field of automated essay scoring (AES), the task of evaluating written compositions has been a persistent challenge. Despite the impressive capabilities of generalized transformer models in various natural language processing (NLP) domains, their application to essay scoring has often fallen short of expectations. In response to this ongoing challenge, this research delves into the intricate nuances of holistic and analytical essay assessment. This work presents an innovative approach centered on Few-Shot transformer-based models, capitalizing on the strengths of pretrained language models while enabling fine-tuning with limited essay-specific data, often called ‘Few-Shot.’ The outcomes of this study are highly promising, with significant improvements in essay scoring accuracy that surpass the performance benchmarks established by conventional methods. The proposed methodology demonstrates remarkable enhancements in the Quadratic Weighted Kappa (QWK) score, indicating its potential. This represents a significant stride towards automating sophisticated essay evaluation, addressing a long-standing issue in the field. |
| format | Article |
| id | doaj-art-27b012ff8d4549649a4bc363becbc05f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-27b012ff8d4549649a4bc363becbc05f2025-01-25T00:02:24ZengIEEEIEEE Access2169-35362025-01-0113124831250110.1109/ACCESS.2025.353027210843186Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based ModelsTahira Amin0Zahoor-Ur-Rehman Tanoli1https://orcid.org/0000-0001-9968-0330Farhan Aadil2https://orcid.org/0000-0001-8737-2154Khalid Mahmood Awan3Sangsoon Lim4https://orcid.org/0000-0001-9924-7115Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computer Engineering, Sungkyul University, Anyang, South KoreaIn the field of automated essay scoring (AES), the task of evaluating written compositions has been a persistent challenge. Despite the impressive capabilities of generalized transformer models in various natural language processing (NLP) domains, their application to essay scoring has often fallen short of expectations. In response to this ongoing challenge, this research delves into the intricate nuances of holistic and analytical essay assessment. This work presents an innovative approach centered on Few-Shot transformer-based models, capitalizing on the strengths of pretrained language models while enabling fine-tuning with limited essay-specific data, often called ‘Few-Shot.’ The outcomes of this study are highly promising, with significant improvements in essay scoring accuracy that surpass the performance benchmarks established by conventional methods. The proposed methodology demonstrates remarkable enhancements in the Quadratic Weighted Kappa (QWK) score, indicating its potential. This represents a significant stride towards automating sophisticated essay evaluation, addressing a long-standing issue in the field.https://ieeexplore.ieee.org/document/10843186/AESNLPtransfer learningBERTfew-shot learningholistic scoring |
| spellingShingle | Tahira Amin Zahoor-Ur-Rehman Tanoli Farhan Aadil Khalid Mahmood Awan Sangsoon Lim Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models IEEE Access AES NLP transfer learning BERT few-shot learning holistic scoring |
| title | Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models |
| title_full | Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models |
| title_fullStr | Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models |
| title_full_unstemmed | Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models |
| title_short | Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models |
| title_sort | enhancing essay scoring an analytical and holistic approach with few shot transformer based models |
| topic | AES NLP transfer learning BERT few-shot learning holistic scoring |
| url | https://ieeexplore.ieee.org/document/10843186/ |
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