Important text characteristics for early-grades text complexity.

Abstract

The Common Core set a standard for all children to read increasingly complex texts throughout schooling. The purpose of the present study was to explore text characteristics specifically in relation to early-grades text complexity. Three hundred fifty primary-grades texts were selected and digitized. Twenty-two text characteristics were identified at 4 linguistic levels, and multiple computerized opera tionalizations were created for each of the 22 text characteristics. A researcher-devised text-complexity outcome measure was based on teacher judgment of text complexity in the 350 texts as well as on student judgment of text complexity as gauged by their responses in a maze task for a subset of the 350 texts. Analyses were conducted using a logical analytical progression typically used in machine-learning research. Random forest regression was the primary statistical modeling technique. Nine text character istics were most important for early-grades text complexity including word structure (decoding demand and number of syllables in words), word meaning (age of acquisition, abstractness, and word rareness), and sentence and discourse-level characteristics (intersentential complexity, phrase diversity, text density/information load, and noncompressibility). Notably, interplay among text characteristics was im portant to explanation of text complexity, particularly for subsets of texts.

Effects of Social-Cognitive Processing Demands and Structural Importance on Narrative Recall: Differences Between Children, Adolescents, and Adults

“…Furthermore, a random intercept over story elements was added to account for general differences in difficulty between the various story elements. For example, word meaning and word structure are known to influence text difficulty (Fitzgerald et al, 2015) and including a random intercept over story elements allowed for these differences to be taken into account. The models were fit with the glmer-function in the lme4-package (Bates, Maechler, Bolker, & Walker, 2014) in R (R Core Team, 2014).…”

An Exploration of Text Difficulty and Knowledge Support on Adolescents' Comprehension

“…Too many concrete details may actually distract a reader from understanding abstract details of a text (Estes, ). Although word concreteness has been associated with increased reading fluency (Tortorelli, ) and comprehension (Fitzgerald et al., ) for young children, no relation was found between word concreteness and reading fluency for adolescents (Barth, Tolar, Fletcher, & Francis, ).…”

Readability indices for the assessment of textbooks: a feasibility study in the context of EFL

“…Some attempts to combine classical features with other linguistic components for the prediction of text complexity have been recently made. Such is the case of Vajjala and Meurers (2012;2014a;2014b), Crossley, Yang, and McNamara (2014), Flor and Beigman (2014), and Fitzgerald et al (2015), among others, who take into account language-specific morphological features or the quantification of coherence and cohesion in a text. Greenfield (1999) found traditional formulae to be predictive of reading difficulty, this was not the case of Brown (1998).…”