Predicting Text Comprehension, Processing, and Familiarity in Adult Readers: New Approaches to Readability Formulas

Crossley, Scott A.; Skalicky, Stephen; Dascălu, Mihai; McNamara, Danielle S.; Kyle, Kristopher

doi:10.1080/0163853x.2017.1296264

Cited by 132 publications

(111 citation statements)

References 54 publications

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“…Textual complexity analyses often include several domains (Crossley et al, 2017). In this paper, comprehension difficulty was quantified in order to infer an objective measure of the complexity of denotative written material in Portuguese using texts related to educational language standards.…”

Section: Discussionmentioning

confidence: 99%

Quantification of textual comprehension difficulty with an information theory-based algorithm

et al. 2019

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Textual comprehension is often not adequately acquired despite intense didactic efforts. Textual comprehension quality is mostly evaluated using subjective criteria. Starting from the assumption that word usage statistics may be used to infer the probability of successful semantic representations, we hypothesized that textual comprehension depended on words with high occurrence probability (high degree of familiarity), which is typically inversely proportional to their information entropy. We tested this hypothesis by quantifying word occurrences in a bank of words from Portuguese language academic theses and using information theory tools to infer degrees of textual familiarity. We found that the lower and upper bounds of the database were delimited by low-entropy words with the highest probabilities of causing incomprehension (i.e., nouns and adjectives) or facilitating semantic decoding (i.e., prepositions and conjunctions). We developed an openly available software suite called CalcuLetra for implementing these algorithms and tested it on publicly available denotative text samples (e.g., articles, essays, and abstracts). We propose that the quantitative model presented here may apply to other languages and could be a tool for supporting automated textual comprehension evaluations, and potentially assisting the development of teaching materials or the diagnosis of learning disorders.

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Section: Discussionmentioning

confidence: 99%

Quantification of textual comprehension difficulty with an information theory-based algorithm

et al. 2019

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“…These linguistic aspects have previously been shown to predict literacy levels in non-clinical corpora [37-38]. NLP tools used to extract these features included the Tool for the Automatic Assessment of Lexical Sophistication (TAALES) [39-40], the Tool for the Automatic Analysis of Cohesion (TAACO) [41], the Tool for the Automatic Assessment of Syntactic Sophistication and Complexity (TAASSC) [42-43], the SEntiment ANalysis and Cognition Engine (séance) [44], and the Writing Assessment Tool (WAT) [45-46].…”

Section: Methodsmentioning

confidence: 99%

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study

Balyan

Crossley

Brown

et al. 2018

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35Limited health literacy can be a barrier to healthcare delivery, but widespread classification of patient health 36 literacy is challenging. We applied natural language processing and machine learning on a large sample of 37 283,216 secure messages sent from 6,941 patients to their clinicians for this study to develop and validate 38 literacy profiles as indicators of patients' health literacy. All patients were participants in Kaiser Permanente 39 Northern California's DISTANCE Study. We created three literacy profiles, comparing performance of each 40 literacy profile against a gold standard of patient self-report. We also analyzed associations between the literacy 41 profiles and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data 42 such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical 43 data such as sex, race, continuous medication gaps and severe hypoglycemia. Literacy profiles varied in their test 44 characteristics, with C-statistics ranging from 0.61-0.74. Relationships between literacy profiles and health 45 outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy 46 profiles as having limited health literacy were older and more likely minority; had poorer medication adherence represents the first successful attempt to use natural language processing and machine learning to measure health 49 literacy. Literacy profiles offer an automated and economical way to identify patients with limited health literacy 50 and a greater vulnerability to poor health outcomes. 51 52 BACKGROUND AND SIGNIFICANCE 53 An estimated 30.3 million people in the U.S. had diabetes mellitus (DM) in 2015 according to the Centers for 54 Disease Control and Prevention (2017). Like most chronic conditions, DM self-management can be complex and 55 requires that patients frequently communicate with healthcare providers. Health literacy (HL) is generally 56 defined as a patient's ability to obtain, process, comprehend and communicate basic health information [1, 2]. 57DM patients with limited HL have a higher risk of poor health outcomes, including worse blood sugar control, 58 higher complication rates [3], and a greater incidence of hypoglycemia [4, 5]. Poor communication and sub-59 optimal adherence to medication may explain some of these disparities [6, 7]. Limited HL contributes to preventable suffering, more rapid decline in physical function [8] and related healthcare costs. Online patient 61 portals embedded within electronic health records (EHRs) are now being used widely to bridge in-person 62 encounters and providing support between visits by allowing patients and providers to communicate via secure 63 messages (SMs). The reach and effectiveness of online communication is likely heavily affected by patients' HL. 64Limited HL is a barrier to use of patient portals and impacts patients' evaluation of online health information [9]. 65However, no research has harnessed S...

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“…The aim is on the recognition of linguistic features that forecast the readability judgments and how the performance of features is when compared to traditional text readability. The findings indicated that the traditional readability formulas are less analytical than text models of text comprehension, and processing from advanced language processing tools (Crossley et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The model explored the space of simplifications, optimizing the reward functions that motivate outputs, which are fluent and simple (Zhang & Lapata, 2017). Research has acknowledged a number of linguistic features that impacts the reading comprehension of new readers; however, limited information is identified about how and whether the findings encompass to adult readers (Crossley et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…A study by Crossley et al, (2017) examined the text processing, comprehensions, and familiarity judgment provided by readers utilize a number of diverse approaches; including machine learning and natural language processing. The aim is on the recognition of linguistic features that forecast the readability judgments and how the performance of features is when compared to traditional text readability.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Language Input on Comprehensiveness of Reading Material among Students in Saudi Arabia

Ali¹

2017

IJLTER

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Abstract. The aim of the study has been to determine the ways that may facilitate the freshmen at universities, who have English as their second language, with comprehension and understanding of study material. It has included different levels of reading material to the students in order to identify which approach is more convenient for the students to perceive. The approach has concluded that advanced vocabulary and grammatical structure may make it difficult for the students to perceive the meaning of study material. It has been perceived from the study that simplification in the text can bring upon positive impacts on the comprehensibility of content. The comprehensiveness can assist students in learning the study modules yet, it is also presumed that simplification may not enhance the students' capability to comprehend the second language more efficiently. It is expected that further research in the field may give deeper insight of the linguistic modifications that may improve the comprehending abilities of the students.

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Predicting Text Comprehension, Processing, and Familiarity in Adult Readers: New Approaches to Readability Formulas

Cited by 132 publications

References 54 publications

Quantification of textual comprehension difficulty with an information theory-based algorithm

Quantification of textual comprehension difficulty with an information theory-based algorithm

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study

Impact of Language Input on Comprehensiveness of Reading Material among Students in Saudi Arabia

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