Abstract
The work presented in this paper is a part of an ongoing project that investigates academic text features indicative of its complexity at different grade levels. In this study we examine comparative complexity of Social science texts used in Russian secondary and high schools. Based on the metrics of ten descriptive and four lexical features assessed for seven classroom textbooks we claim lexical diversity, frequency, abstractness and the number of terminological units to be statistically significant predictors of text complexity. The total size of the Corpus of over 160.000 tokens comprising two sets of textbooks ranging from the 5th to the 11th grades provides a satisfactory level of its representativeness and as such a solid foundation for statistical validity of the results. We employ RusAC, an online text analyzer, to compute lexical features of texts and the effect of the four lexical features on text complexity is confirmed with a mixed analysis of variance. The study fills a gap both in corpus linguistics as regards a systematic approach to Russian academic texts and in text complexity studies as regards the description of secondary and high school textbooks.
Highlights
As a focus of numerous studies for over fifty years, the problem of assessment of Russian texts linguistic complexity is still viewed theoretically valuable [1,2,3]
The three lexical features with the highest impact on academic text complexity validated in the recent studies are lexical diversity, frequency and abstractness [5]
In 1981, Anderson & Freebody claimed the ratio of difficult words in a reading text to be the best predictor of text complexity [18]. Another feature directly influencing text complexity is lexical frequency: the more high frequency words are used in the text, the easier it is for the reader
Summary
As a focus of numerous studies for over fifty years, the problem of assessment of Russian texts linguistic complexity is still viewed theoretically valuable [1,2,3]. The research in the area is aimed at designing an algorithm identifying a “target reading audience” and validating a list of text features which effect its complexity. The latter is especially significant nowadays due to the increased information flow and cognitive density of modern academic texts [4]. The three lexical features with the highest impact on academic text complexity validated in the recent studies are lexical diversity, frequency and abstractness [5]. The total count of terms is viewed as an additional predictor of text complexity in studies on reading comprehension [6, 7]
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