Affiliations: [a] Research and Education Center on Linguistics named after I.A. Boduen de Kurtene, Kazan Federal University, Kazan, Russian Federation
| [b] Department of German Philology, Higher School of Russian and Foreign Philology, Kazan Federal University, Russian Federation
| [c]
Innopolis University, 1, Universitetskaya Str., Innopolis, Russian Federation
| [d] Centro de Investigación en Computación, Instituto Politécnico Nacional, CDMX, Mexico
Correspondence:
[*]
Corresponding author. Valery Solovyev, Research and Education Center on Linguistics named after I.A. Boduen de Kurtene, Kazan
Federal University, 18 Kremlyovskaya street, Kazan 420008, Russian Federation. Tel.: +7 843 233 75 12; Fax: +7
843 292 74 18; E-mail: [email protected].
Abstract: 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.
Keywords: Text readability, machine learning, Russian academic text, text
complexity, examination tests
