An Automated Approach to Selecting Sentences for Test Case Generation

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Аннотация

The modern field of education is characterized by the increasing use of multiple choice tests to assess students’ knowledge and skills. One of the common methods of selecting sentences for such tests is the application of textual data clustering procedures. In this study, a module for sentence selection was developed that includes three steps: preprocessing, sentence parameter computation, and clustering. However, an objective evaluation of the quality of the obtained clusters using the silhouette coefficient and Davis-Boldin index showed that the clustering model used did not give satisfactory results.

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Авторлар туралы

Maria Maslova

Volzhsky Polytechnic Institute (branch) of Volgograd State Technical University

Хат алмасуға жауапты Автор.
Email: miss.mari.m@inbox.ru
ORCID iD: 0000-0003-3845-3972
SPIN-код: 2933-6263

senior teacher, Department of Computer Science and Programming Technology

Ресей, Volzhsky

Әдебиет тізімі

  1. Bholowalia P., Arvind K. EBK-means: A clustering technique based on elbow method and K-means in WSN. International Journal of Computer Applications. 2014. No. 105. Pp. 17–24.
  2. Das B., Majumder M., Phadikar S., Ahmed S.A. Automatic generation of fill-in-the-blank question with corpus-based distractors for E-assessment to enhance learning. Computer Applications in Engineering Education. 2019. No. 27. Pp. 1485–1495.
  3. Das B., Majumder M., Phadikar S., Sekh A.A. Multiple-choice question generation with auto-generated distractors for computer-assisted educational assessment. Multimedia Tools and Applications. 2021. No. 80. Pp. 31907–31925. doi: 10.1007/s11042-021-11222-2
  4. Riza L.S., Firdaus Y., Sukamto R.A., Samah W.Kh.A.F.A. Automatic generation of short-answer questions in reading comprehension using NLP and KNN. Multimedia Tools and Applications. 2023. No. 82. Pp. 41913–41940. doi: 10.1007/s11042-023-15191-6
  5. Bulyga F.S., Kureichik V.M. Clustering of the text document corpus using the k-means algorithm. News of Universities. North-Caucasian Region. Technical Sciences. 2022. No. 3. Pp. 33–40. (In Rus.) doi: 10.17213/1560-3644-2022-3-33-40
  6. Walter A.I. Methodics of development of test tasks of control-measuring materials. News of TulSU. Technical Sciences. 2022. No. 3. (In Rus.) URL: https://cyberleninka.ru/article/n/metodika-razrabotki-testovyh-zadaniy-kontrolno-izmeritelnyh-materialov
  7. Mizernov I.Yu., Grashchenko L.A. Analysis of methods for assessing text complexity. New Information Technologies in Automated Systems. 2015. No. 18 (In Rus.) URL: https://cyberleninka.ru/article/n/analiz-metodov-otsenki-slozhnosti-teksta
  8. Yatsko V.A. Stop-words as a basis for classification of text documents. Actual Problems of Applied Mathematics, Informatics and Mechanics. 2021. Pp. 486–492 (In Rus.)

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Әрекет
1. JATS XML
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2. Fig. 1. Custering of sentences by five parameters

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Метадеректер
3. Fig. 2. Clustering of sentences by two parameters

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