Electronic Database of Non-Native Speakers’ Speech Errors as a Scientific and Educational Resource (on the Example of Russian Schools)
Elena V. Grudeva1, Irina A. Buchilova2, Alina A. Diveeva3, Elena M. Ivanova4, Oleg L. Selyanichev5, Maxim S. Trapeznikov6
1Elena V. Grudeva*, Doctor of Philology Sciences, Professor, Department of National Philology and Applied Communications, Cherepovets State University, Cherepovets, Russia.
2A. Buchilova, PhD in Psychology Sciences, Associate Professor, Department of National Philology and Applied Communications, Cherepovets State University, Cherepovets, Russia.
3Alina A. Diveeva, Master of Philology, Post-graduate student, Department of National Philology and Applied Communications, Cherepovets State University, Cherepovets, Russia.
4Elena M. Ivanova, PhD in Philology Sciences, Associate Professor, Department of Social Communications and Media, Cherepovets State University, Cherepovets, Russia.
5Oleg L. Selvanichev, PhD in Technology Sciences, Associate Professor, Department of Mathematical and Software Computer, Cherepovets State University, Cherepovets, Russia.
6Maxim S. Trapeznikov, Student, Department of Mathematical and Software Computer, Cherepovets State University, Cherepovets, Russia.
Manuscript received on November 19, 2019. | Revised Manuscript received on 25 November, 2019. | Manuscript published on December 10, 2019. | PP: 1557-1561 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7262129219/2019©BEIESP | DOI: 10.35940/ijitee.B7262.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The article is devoted to the development of an electronic database of speech errors of non-native speakers studying in Russian schools. The database of non-native speakers’ speech errors developed by the authors includes the following parameters: 1) name of the non-native speaker, 2) age, 3) gender, 4) native language, 5) target word / phrase, 6) context1 (context that includes an error), 7) normative spelling, 8) context2 (context with a corrected error), 9) type of text in which the error occurred, 10) category of error, 11) type of error, 12) year of the material collection. Database pattern, as well as database program, which are a client-server Web application, are presented in the article. The study involved 87 non-native speakers of 11 nationalities: Azerbaijanis (25), Uzbeks (13), Ukrainians (13), Kyrghyz (11), Armenians (11), Tajiks (5), Dargins (2), Avars (2), Talysh (2), Belarusians (2), and Vietnamese (1). The authors have collected and systematized educational written texts of 4 types (dictations, essays, summaries, and copyings) of non-native speakers studying in general education schools of the city of Cherepovets of the Vologda Region. At the moment, there are 239 texts, among which: dictations comprise 96, summary – 26, essays – 41, copying – 76. In these texts, 2039 non-normative spellings were found and qualified. The authors of the project introduced a multidimensional system for the classification of errors that were identified in the written texts of the non-native speakers. At the first level, the following types of errors were distinguished: 1) nonspelling, 2) graphic, 3) spelling, 4) lexical, and 5) grammatical. Research and educational problems that can be solved by means of this resource are considered.
Keywords: Electronic Database, Russian Language, Speech Errors, Non-Native Speakers, Written Texts
Scope of the Article: Database Theory and Application