A survey of models for automatic assessment of similarity of student's answer to the reference answer
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| Názov: | A survey of models for automatic assessment of similarity of student's answer to the reference answer |
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| Autori: | Nadezhda S. Lagutina, Ksenia V. Lagutina |
| Zdroj: | Моделирование и анализ информационных систем, Vol 32, Iss 1, Pp 42-65 (2025) |
| Informácie o vydavateľovi: | P.G. Demidov Yaroslavl State University, 2025. |
| Rok vydania: | 2025 |
| Predmety: | text classification, assessing students' answers, artificial intelligence in education, neural network language models, Information technology, natural language processing, T58.5-58.64, text similarity |
| Popis: | The development of automatic assessment systems is a relevant task designed to simplify the routine work of a teacher and speed up feedback for a student. The survey is devoted to research in the field of automatic assessment of student answers based on a teacher's reference answer. The authors of the work analyzed text models used for the tasks of automatic assessment of short answers (ASAG) and automated essay assessment (AES). Several approaches were also taken into account for the task of determining the text similarity, since it is a close task, and the methods for solving it can also be useful for analyzing student answers. Text models can be divided into several large categories. The first is linguistic models based on various stylometric features, both simple ones like a bag of words and n-grams, and complex ones like syntactic and semantic ones. The authors attributed neural network models based on various embeddings to the second category. It highlights large language models as universal, popular and high-quality modeling methods. The third category includes combined models that unite both linguistic features and neural network embeddings. A comparison of modern studies on models, methods and quality metrics showed that the trends in the subject area coincide with the trends in computational linguistics in general. A large number of authors choose large language models to solve their problems, but standard features remain in demand. It is impossible to single out a universal approach; each subtask requires a separate choice of method and adjustment of its parameters. Combined and ensemble approaches allow achieving higher quality than other methods. The vast majority of studies examine texts in English. However, successful results for national languages are also found. It can be concluded that the development and adaptation of methods for assessing students' answers in national languages is a relevant and promising task. |
| Druh dokumentu: | Article |
| ISSN: | 2313-5417 1818-1015 |
| DOI: | 10.18255/1818-1015-2025-1-42-65 |
| Prístupová URL adresa: | https://doaj.org/article/3969c0f531dc47fca95c1206e66831bb |
| Rights: | URL: https://www.mais-journal.ru/jour/about/editorialPolicies#openAccessPolicy |
| Prístupové číslo: | edsair.doi.dedup.....3c6de6eb16a14a1c451e777819b23113 |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | The development of automatic assessment systems is a relevant task designed to simplify the routine work of a teacher and speed up feedback for a student. The survey is devoted to research in the field of automatic assessment of student answers based on a teacher's reference answer. The authors of the work analyzed text models used for the tasks of automatic assessment of short answers (ASAG) and automated essay assessment (AES). Several approaches were also taken into account for the task of determining the text similarity, since it is a close task, and the methods for solving it can also be useful for analyzing student answers. Text models can be divided into several large categories. The first is linguistic models based on various stylometric features, both simple ones like a bag of words and n-grams, and complex ones like syntactic and semantic ones. The authors attributed neural network models based on various embeddings to the second category. It highlights large language models as universal, popular and high-quality modeling methods. The third category includes combined models that unite both linguistic features and neural network embeddings. A comparison of modern studies on models, methods and quality metrics showed that the trends in the subject area coincide with the trends in computational linguistics in general. A large number of authors choose large language models to solve their problems, but standard features remain in demand. It is impossible to single out a universal approach; each subtask requires a separate choice of method and adjustment of its parameters. Combined and ensemble approaches allow achieving higher quality than other methods. The vast majority of studies examine texts in English. However, successful results for national languages are also found. It can be concluded that the development and adaptation of methods for assessing students' answers in national languages is a relevant and promising task. |
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| ISSN: | 23135417 18181015 |
| DOI: | 10.18255/1818-1015-2025-1-42-65 |