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
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
Reference list:
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.
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.
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
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Keywords:
automatic test generation, automatic test question generation, natural language processing, clustering.


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