An automated approach to selecting sentences for test case generation

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.