Ablation study on feature group importance for automated essay scoring
Grading of written academic essays by humans requires significant effort. It is a time-consuming task and is vulnerable to human biases. Ever since the introduction of modern computing, this has been one of the many automations being explored. Researches in automated essay scoring have been on-going...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/19430/ http://journalarticle.ukm.my/19430/1/08.pdf |
| _version_ | 1848814839026155520 |
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| author | Tan, Jih Soong Tan, Ian K.T. |
| author_facet | Tan, Jih Soong Tan, Ian K.T. |
| author_sort | Tan, Jih Soong |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Grading of written academic essays by humans requires significant effort. It is a time-consuming task and is vulnerable to human biases. Ever since the introduction of modern computing, this has been one of the many automations being explored. Researches in automated essay scoring have been on-going, where the majority of the researches in recent years are based on extracting multiple linguistic features and using them to build a classification model for automated essay scoring. The 3 main types of features used are lexical, grammatical, and semantic. In our work, we conducted an ablation study to discover the engineered features that has the weakest influence. We did this using a generic feature engineering and classification approach that was used by the winners of the Automated Student Assessment Prize (ASAP). This is to mitigate biases that may have addressed specific feature engineering or models. Our results show that a semantic feature called the prompt has been the weakest feature in influencing the models. From further investigations, this was due to it being over-fitted in the classification model. |
| first_indexed | 2025-11-15T00:40:27Z |
| format | Article |
| id | oai:generic.eprints.org:19430 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:40:27Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:194302022-08-18T08:21:37Z http://journalarticle.ukm.my/19430/ Ablation study on feature group importance for automated essay scoring Tan, Jih Soong Tan, Ian K.T. Grading of written academic essays by humans requires significant effort. It is a time-consuming task and is vulnerable to human biases. Ever since the introduction of modern computing, this has been one of the many automations being explored. Researches in automated essay scoring have been on-going, where the majority of the researches in recent years are based on extracting multiple linguistic features and using them to build a classification model for automated essay scoring. The 3 main types of features used are lexical, grammatical, and semantic. In our work, we conducted an ablation study to discover the engineered features that has the weakest influence. We did this using a generic feature engineering and classification approach that was used by the winners of the Automated Student Assessment Prize (ASAP). This is to mitigate biases that may have addressed specific feature engineering or models. Our results show that a semantic feature called the prompt has been the weakest feature in influencing the models. From further investigations, this was due to it being over-fitted in the classification model. Penerbit Universiti Kebangsaan Malaysia 2022-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19430/1/08.pdf Tan, Jih Soong and Tan, Ian K.T. (2022) Ablation study on feature group importance for automated essay scoring. Asia-Pacific Journal of Information Technology and Multimedia, 11 (1). pp. 90-101. ISSN 2289-2192 https://www.ukm.my/apjitm/articles-issues |
| spellingShingle | Tan, Jih Soong Tan, Ian K.T. Ablation study on feature group importance for automated essay scoring |
| title | Ablation study on feature group importance for automated essay scoring |
| title_full | Ablation study on feature group importance for automated essay scoring |
| title_fullStr | Ablation study on feature group importance for automated essay scoring |
| title_full_unstemmed | Ablation study on feature group importance for automated essay scoring |
| title_short | Ablation study on feature group importance for automated essay scoring |
| title_sort | ablation study on feature group importance for automated essay scoring |
| url | http://journalarticle.ukm.my/19430/ http://journalarticle.ukm.my/19430/ http://journalarticle.ukm.my/19430/1/08.pdf |