Ihor Drahushchak, O. Taran, S. Bybyk, Olesya V. Saban, N. M. Sharmanova
{"title":"Software for Measuring Linguistic Literacy Rate of Students (Based on Comments Written in Ukrainian)","authors":"Ihor Drahushchak, O. Taran, S. Bybyk, Olesya V. Saban, N. M. Sharmanova","doi":"10.1145/3526242.3526248","DOIUrl":null,"url":null,"abstract":"In this article, linguistic literacy rate is measured by the number of errors in students’ comments on the web portal. The data comprising about 10,000 comments covering all regions of Ukraine over a period of 10 years has been analyzed. The stages of creating a software which interacts with the LanguageTool and enables generating the results of error analysis and classifying them by types and regions have been described. A map of linguistic literacy of Ukrainian students has been created. Also, the regions with the highest and lowest linguistic literacy and the main types of errors have been identified. The obtained data will make it possible to revise and adjust university language teaching programs in each region in the future.","PeriodicalId":288048,"journal":{"name":"Digital Humanities Workshop","volume":"301 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Humanities Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3526242.3526248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this article, linguistic literacy rate is measured by the number of errors in students’ comments on the web portal. The data comprising about 10,000 comments covering all regions of Ukraine over a period of 10 years has been analyzed. The stages of creating a software which interacts with the LanguageTool and enables generating the results of error analysis and classifying them by types and regions have been described. A map of linguistic literacy of Ukrainian students has been created. Also, the regions with the highest and lowest linguistic literacy and the main types of errors have been identified. The obtained data will make it possible to revise and adjust university language teaching programs in each region in the future.