An Automated Approach to Selecting Sentences for Test Case Generation
( Pp. 29-34)
More about authors
Maslova Maria A.
senior teacher, Department of Computer Science and Programming Technology
Volzhsky Polytechnic Institute (branch) of Volgograd State Technical University
Volzhsky, Russian Federation
Volzhsky Polytechnic Institute (branch) of Volgograd State Technical University
Volzhsky, Russian Federation
Abstract:
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.
How to Cite:
Maslova M.A. An Automated Approach to Selecting Sentences for Test Case Generation. Computational Nanotechnology. 2024. Vol. 11. No. 2. Pp. 29–34. (In Rus.). DOI: 10.33693/2313-223X-2024-11-2-29-34. EDN: MHKRNS
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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
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
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
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
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
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.)
Keywords:
automatic test generation, automatic test question generation, natural language processing, clustering.
