Classification of Russian textbooks by grade level and topic using ReaderBench

This article analyzes automated methods for classifying Russian-language textbooks in two dimensions, and precisely on the topic of the text and its complexity, reflected by the corresponding school level (class

Deictic elements as means of text cohesion and coherence in academic discourse

© 2017, Association for Social Studies Educa. All rights reserved. The article presents the results of the research aimed at analyzing some functions and features of deictic elements in academic discourse in English. The material under analysis covers 20 academic texts written by English-speaking linguists. In the article it is proved that in academic discourse deictic elements can operate only within the fixed scheme of deictic coordinates, which has got three main elements: deictic center, deictic element, and antecedent/subsequent element. Out of this scheme deictic elements fail to fulfill referential procedure. All deictic elements in academic discourse are divided into two big groups: conventional deictic elements and endemic ones. The result of the research shows that conventional deictic elements in most cases provide text cohesion (within small text units, such as adjoining sentences); whereas endemic deictic elements tend to serve for text coherence (in larger text units, such as paragraphs, chapters, etc.). Thus, deictic elements can be considered important units providing textbuilding

Discourse complexity: driving forces of the new paradigm

In the article, the authors investigate what makes the text difficult for a certain category of readers, thereby expanding the object of research from " text" to "text and reader", or, more specifically, "alignment of text and reader". Texts are examined for complexity, i.e. features of the text that affect its understandin

Investigating the Differences Between Prepared and Spontaneous Speech Characteristics: Descriptive Approach

In the modern EFL paradigm, pre-task planning time is viewed as a norm. Pre-task planning time is one of the central concerns of teachers, test-developers, as well as researchers. Pre-task planning is planning a speech before performing a task, and it also involves rehearsal and strategic planning. The paper addresses the problem of pre-task planning advisability for A2 Russian EFL speakers. The research presented in this paper examines the structure, breakdown, repair, syntactic complexity, lexical diversity as well as the accuracy of the discourse produced by 145 Russian participants of the English language competition held in Kazan, Russia, in January 2020. The discourse analysis revealed that the pre-task time is used by A2 EFL speakers not only to focus on a dialog but also to elicit a topic text from memory, thus focusing on form rather than meaning. Hence, in A2 tests prioritizing meaning over form and measuring the ability for spontaneous speech, the one-minute pre-task planning time is viewed as questionable

Lexical and syntactic features of academic Russian texts: a discriminant analysis

This article presents three mathematical models to differentiate academic texts from three subject discourses written in Russian (i.e., Philological, Mathematical, and Natural Sciences) which further enable design and automated profiling of corresponding typologie

Lexical density as a complexity predictor: the case of Science and Social Studies textbooks

The presented study aims to study the lexical density, interpreted by the authors as an effective predictor of text complexity and calculated by the ratio of words of significant parts of speech to the common the number of words in the text. The study also aims to study the dynamics and correlations of the Flesch-Kincaid index (readability) with the lexical density in the texts of 12 textbooks on natural and social sciences, taught in grades 7-12 in American school

Deictic elements as means of text cohesion and coherence in academic discourse

© 2017, Association for Social Studies Educa. All rights reserved. The article presents the results of the research aimed at analyzing some functions and features of deictic elements in academic discourse in English. The material under analysis covers 20 academic texts written by English-speaking linguists. In the article it is proved that in academic discourse deictic elements can operate only within the fixed scheme of deictic coordinates, which has got three main elements: deictic center, deictic element, and antecedent/subsequent element. Out of this scheme deictic elements fail to fulfill referential procedure. All deictic elements in academic discourse are divided into two big groups: conventional deictic elements and endemic ones. The result of the research shows that conventional deictic elements in most cases provide text cohesion (within small text units, such as adjoining sentences); whereas endemic deictic elements tend to serve for text coherence (in larger text units, such as paragraphs, chapters, etc.). Thus, deictic elements can be considered important units providing textbuilding

Computing syntactic parameters for automated text complexity assessment

Copyright © 2019 for this paper by its authors. The article focuses on identifying, extracting and evaluating syntactic parameters influencing the complexity of Russian academic texts. Our ultimate goal is to select a set of text features effectively measuring text complexity and build an automatic tool able to rank Russian academic texts according to grade levels. models based on the most promising features by using machine learning methods The innovative algorithm of designing a predictive model of text complexity is based on a training text corpus and a set of previously proposed and new syntactic features (average sentence length, average number of syllables per word, the number of adjectives, average number of participial constructions, average number of coordinating chains, path number, i.e. average number of sub-trees). Our best model achieves an MSE of 1.15. Our experiments indicate that by adding the abovementioned syntactic features, namely the average number of participial constructions, average number of coordinating chains, and the average number of sub-trees, the text complexity model performance will increase substantially

Prediction of reading difficulty in Russian academic texts

© 2019 - IOS Press and the authors. All rights reserved. Education policy makers viewmeasuring 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

Computing syntactic parameters for automated text complexity assessment

Copyright © 2019 for this paper by its authors. The article focuses on identifying, extracting and evaluating syntactic parameters influencing the complexity of Russian academic texts. Our ultimate goal is to select a set of text features effectively measuring text complexity and build an automatic tool able to rank Russian academic texts according to grade levels. models based on the most promising features by using machine learning methods The innovative algorithm of designing a predictive model of text complexity is based on a training text corpus and a set of previously proposed and new syntactic features (average sentence length, average number of syllables per word, the number of adjectives, average number of participial constructions, average number of coordinating chains, path number, i.e. average number of sub-trees). Our best model achieves an MSE of 1.15. Our experiments indicate that by adding the abovementioned syntactic features, namely the average number of participial constructions, average number of coordinating chains, and the average number of sub-trees, the text complexity model performance will increase substantially