Suchergebnisse

Reading comprehension relies on a variety of complex skills that are not effectively assessed by existing Russian language tests. At the same time, Russian textbooks are criticized both for their low text quality and high text complexity. This study addresses issues of Russian language proficiency and comprehension assessment with the development of the Russian Language Test (RLT). The RLT was constructed to measure proficiency relevant to textbook comprehension, such as grammar and vocabulary knowledge, establishing propositional meaning and inferencing. Results from this initial study including 81 fifth-grade and 94 ninth-grade students confirm that students struggle with grammatical inferences and identifying the main idea in a text. Additionally, three standardized Russian exams, VPR, OGE, EGE are analyzed, affording an overview of the testing system for the Russian language from the elementary through high school education levels. This study demonstrates promise for the use of the RLT as a language proficiency assessment and provides a broad context for understanding the current state of Russian language tests for native speakers.

The article is aimed at considering the issue of the discursive text typology and developing a parametric model of the elementary school texts for the ontological domain by employing a corpus-based approach and methods of linguistic statistics. The research corpus of over 90,000 tokens comprises texts of 13 textbooks acknowledged in the 2 nd grade of Russian schools. The applied multifactor discriminant analysis enabled identification and validation of typological characteristics of the texts under study, offering the formula for referring educational texts to a subject domain on Philology, Mathematics, and Natural Sciences. The discriminant analysis results confirmed the hypothesis that each type of text corresponds to a parametric model, which includes six constants: the average number of words in a sentence, the average number of nouns, the average number of verbs and the average number of adjectives per sentence, local noun overlap, global argument overlap. The assessment of linguistic parameters was performed by an automatic Russian text analyzer RuLingva. The classification accuracy of the parametric model was identified as 80%, which ensures its high reliability and allows for the data obtained to be employed in linguistic expertise, as well as for in automated linguistic profiling of texts. The prospect of the research implies installation of the model in RuLingva and development of similar models for texts of other subject domains.