Казанский (Приволжский) федеральный университет, КФУ
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PREDICTION OF READING DIFFICULTY IN RUSSIAN ACADEMIC TEXTS
Форма представленияСтатьи в зарубежных журналах и сборниках
Год публикации2019
Языканглийский
  • Солнышкина Марина Ивановна, автор
  • Соловьев Валерий Дмитриевич, автор
  • Библиографическое описание на языке оригинала Solovyev Valery, Solnyshkina Marina, Ivanov Vladimir, Prediction of reading difficulty in Russian academic texts//Journal of Intelligent & Fuzzy Systems. - 2019. - Vol.36, Is.5. - P.4553-4563.
    Аннотация Education policy makers view measuring academic texts readability and profiling classroom textbooks as a primary task of education management aimed at sustaining quality of reading programs. As Russian readability metrics, i.e. “objective” features of texts determining its complexity for readers, are still a research niche, we undertook a comparative analysis of academic texts features exemplified in textbooks on Social Science and examination texts of Russian as a foreign language. Experiments for 7 classifiers and 4 methods of linear regression on Russian Readability corpus demonstrated that ranking textbooks for native speakers is a much more difficult task than ranking examination texts written (or designed) for foreign students. The authors see a possible reason for this in differences between two processes: acquiring a native language on the one hand and learning a foreign language on the other. The results of the current study are extremely relevant in modern Russia which is joining the Bologna Process and needs to provide profiled texts for all types of learners and testees. Based on a qualitative and quantitative analysis of a text, the research offers a guide for education managers to help build consensus on selecting a reading material when educators have differing views.
    Ключевые слова Text readability, machine learning, Russian academic text, text complexity, examination tests
    Название журнала JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
    URL https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179007
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