A survey of models for automatic assessment of similarity of student's answer to the reference answer
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...
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| Format: | Article |
| Language: | English |
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Yaroslavl State University
2025-03-01
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| Series: | Моделирование и анализ информационных систем |
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| Online Access: | https://www.mais-journal.ru/jour/article/view/1915 |
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| author | Nadezhda S. Lagutina Ksenia V. Lagutina |
| author_facet | Nadezhda S. Lagutina Ksenia V. Lagutina |
| author_sort | Nadezhda S. Lagutina |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3969c0f531dc47fca95c1206e66831bb |
| institution | DOAJ |
| issn | 1818-1015 2313-5417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Yaroslavl State University |
| record_format | Article |
| series | Моделирование и анализ информационных систем |
| spelling | doaj-art-3969c0f531dc47fca95c1206e66831bb2025-08-20T03:00:45ZengYaroslavl State UniversityМоделирование и анализ информационных систем1818-10152313-54172025-03-01321426510.18255/1818-1015-2025-1-42-651427A survey of models for automatic assessment of similarity of student's answer to the reference answerNadezhda S. Lagutina0Ksenia V. Lagutina1P.G. Demidov Yaroslavl State UniversityP.G. Demidov Yaroslavl State UniversityThe 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.https://www.mais-journal.ru/jour/article/view/1915natural language processingtext similaritytext classificationneural network language modelsassessing students' answersartificial intelligence in education |
| spellingShingle | Nadezhda S. Lagutina Ksenia V. Lagutina A survey of models for automatic assessment of similarity of student's answer to the reference answer Моделирование и анализ информационных систем natural language processing text similarity text classification neural network language models assessing students' answers artificial intelligence in education |
| title | A survey of models for automatic assessment of similarity of student's answer to the reference answer |
| title_full | A survey of models for automatic assessment of similarity of student's answer to the reference answer |
| title_fullStr | A survey of models for automatic assessment of similarity of student's answer to the reference answer |
| title_full_unstemmed | A survey of models for automatic assessment of similarity of student's answer to the reference answer |
| title_short | A survey of models for automatic assessment of similarity of student's answer to the reference answer |
| title_sort | survey of models for automatic assessment of similarity of student s answer to the reference answer |
| topic | natural language processing text similarity text classification neural network language models assessing students' answers artificial intelligence in education |
| url | https://www.mais-journal.ru/jour/article/view/1915 |
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