Complexity of the text: A literature teacher by A.Kakhkhor
Keywords:
text complexity, Readability, Reading levels, Flesch Reading Ease (FRE), Flesch–Kincaid, Grade Level (FKGL), Lexical familiarity, Syntactic complexity, Comprehension metrics, Pedagogical assessment, Natural Language Processing (NLP), Empirical validation, Statistical readability, Lexical sophistication,Abstract
The present study explores the historical evolution and methodological underpinnings of text complexity assessment, highlighting the seminal contributions of Emmett Betts, Edgar Dale, Jeanne Chall, Rudolf Flesch, and J. Peter Kincaid. The research aims to provide a comprehensive understanding of how readability and linguistic accessibility have been quantified from pedagogical observation to computational modeling. Methodologically, the paper synthesizes key empirical findings and formulaic frameworks that have shaped readability measurement across decades. Betts’s classification of reading proficiency in the 1940s established a pedagogical model correlating oral reading accuracy with comprehension, while Dale and Chall’s statistical analyses in the mid-twentieth century identified lexical familiarity and sentence length as the primary predictors of textual difficulty. Building upon these foundations, Flesch and later Flesch–Kincaid developed quantitative indices—Reading Ease and Grade Level—to operationalize syntactic and morphological complexity through measurable linguistic variables. As an applied component, the study conducts a readability evaluation of the short story “ Literature Teacher,” using the Flesch–Kincaid formulas to illustrate the practical implementation of these metrics in literary discourse analysis. The findings underscore both the enduring value and the intrinsic limitations of these indices, particularly their neglect of conceptual coherence, domain-specific vocabulary, and visual-textual components. Recent scholarly consensus (2020–2025) reaffirms the relevance of readability formulas in computational linguistics and Natural Language Processing (NLP), while emphasizing the necessity of integrating automated measures with expert linguistic evaluation. The study concludes that a hybrid approach—combining quantitative readability metrics with qualitative human judgment—offers a more precise and contextually valid framework for assessing text complexity.
