In this study, a corpus of expert-graded essays, based on a standardized scoring rubric, is computationally evaluated so as to distinguish the differences between those essays that were rated as high and those rated as low. The automated tool, Coh-Metrix, is used to examine the degree to which high- and low-proficiency essays can be predicted by linguistic indices of cohesion (i.e., coreference and connectives), syntactic complexity (e.g., number of words before the main verb, sentence structure overlap), the diversity of words used by the writer, and characteristics of words (e.g., frequency, concreteness, imagability). The three most predictive indices of essay quality in this study were syntactic complexity (as measured by number of words before the main verb), lexical diversity (as measured by the Measure of Textual Lexical Diversity), and word frequency (as measured by Celex, logarithm for all words). Using 26 validated indices of cohesion from Coh-Metrix, none showed differences between high- and low-proficiency essays and no indices of cohesion correlated with essay ratings. These results indicate that the textual features that characterize good student writing are not aligned with those features that facilitate reading comprehension. Rather, essays judged to be of higher quality were more likely to contain linguistic features associated with text difficulty and sophisticated language.

Prior knowledge is one of the strongest contributors to comprehension, but there is little specificity about different aspects of prior knowledge and how they impact comprehension. This article introduces the Multidimensional Knowledge in Text Comprehension framework, which conceptualizes prior knowledge along four intersecting dimensions: amount, accuracy, specificity, and coherence. Amount refers to how many relevant concepts the reader knows. Accuracy refers to the extent to which the reader’s knowledge is correct. Specificity refers the degree to which the knowledge is related to information in the target text. Coherence refers to the interconnectedness of prior knowledge. Conceptualizing prior content knowledge along these dimensions deepens understanding of the construct and lends to more specific predictions about how learners process information. Considering knowledge across multiple dimensions is crucially important to the development and selection of prior knowledge assessments and, in turn, educators’ ability to capitalize on learners’ strengths across various comprehension tasks.

This study demonstrates the generalization of previous laboratory results showing the benefits of Self-Explanation Reading Training (SERT) to college students’ course exam performance. The participants were 265 students enrolled in an Introductory Biology course, 59 of whom were provided with SERT. The results showed that SERT benefited students who began the course with less knowledge about science but did not benefit students with greater prior science knowledge. Moreover, across the three exams in the course, low-knowledge students who received SERT performed as well as high-knowledge students, whereas low-knowledge students without SERT performed more poorly than high-knowledge students. Hence, instruction on how to self-explain and use comprehension strategies allowed low-knowledge students to overcome their knowledge deficits. These results provide further evidence that self-explanation in combination with instruction and practice using comprehension strategies helps students to more effectively process and understand science.

Texts are routinely simplified for language learners with authors relying on a variety of approaches and materials to assist them in making the texts more comprehensible. Readability measures are one such tool that authors can use when evaluating text comprehensibility. This study compares the Coh-Metrix Second Language (L2) Reading Index, a readability formula based on psycholinguistic and cognitive models of reading, to traditional readability formulas on a large corpus of texts intuitively simplified for language learners. The goal of this study is to determine which formula best classifies text level (advanced, intermediate, beginner) with the prediction that text classification relates to the formulas’ capacity to measure text comprehensibility. The results demonstrate that the Coh-Metrix L2 Reading Index performs significantly better than traditional readability formulas, suggesting that the variables used in this index are more closely aligned to the intuitive text processing employed by authors when simplifying texts.