3,886 research outputs found

    The effects of individual differences and linguistic features on reading comprehension of health-related texts

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    Background. Relatively little attention has been focused on whether or how the effects of reader characteristics, or of the linguistic properties of a text, predict reading comprehension of health-related information. In addition, there is little evidence for the utility of any of the writing guidelines promulgated by the National Health Service (NHS) in order to improve the comprehension of health information. Nonetheless, some previous research suggests that health-related texts could be adapted for different groups of users to optimise understanding. Thus, existing knowledge presents important limitations, and raises concerns with potentially far-reaching practical implications. To address these concerns, I investigated how variation in individual differences and in text features predicts the comprehension of health-related texts, examining how the effects of textual features may differ for different kinds of readers. Method. The focus of this thesis is on Study 3, in which I investigated the predictors of tested comprehension, but I report preliminary studies where I examined the readability of a sample of health-related texts (Study 1), and the perceived comprehension of a sample of health-related texts (Study 2). In the primary study (Study 3), I used Bayesian mixed-effects models to analyse the influences that affect the accuracy of responses to questions probing the comprehension of a sample of health-related texts. I measured variation among 200 participants in their cognitive abilities, to capture the effects of individual differences, as well as variation in the linguistic features of texts, to capture the effects of text structure and content. Results. I found that tested comprehension was less likely to be accurate among older participants. However, comprehension accuracy was greater given higher levels of education, health literacy, and English language proficiency levels. In addition, self-rated evaluations of perceived comprehension predicted comprehension, but only in the absence of other individual-differences-related predictors. Variation in text features, including readability estimates, did not predict comprehension accuracy, and there was no evidence for the modulation of the effects of individual differences by text features. Discussion. Text features did not module the effects of individual differences to influence comprehension accuracy in any meaningful way. This suggests that adapting health-related texts to different groups of the population may be of limited practical value. Implications. Individual differences really matter to comprehension. Thus, optimally, understanding of health-related texts amongst the end-users should be tested, and interventions to aid readers, such as those with relatively low health literacy levels, could be used to improve comprehension of health-texts. In the absence of sensitive measures of reader characteristics, and when testing of understanding is not possible, the use of end-user evaluations of health-related texts may serve as a useful proxy of tested comprehension. However, looking for text effects, and guidance focusing on text effects, seems less useful given the reported evidence. Consequently, the effectiveness of designing health-related texts with the consideration of NHS’s text writing guidelines, is likely to be limited

    Translationese and post-editese : how comparable is comparable quality?

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    Whereas post-edited texts have been shown to be either of comparable quality to human translations or better, one study shows that people still seem to prefer human-translated texts. The idea of texts being inherently different despite being of high quality is not new. Translated texts, for example,are also different from original texts, a phenomenon referred to as ‘Translationese’. Research into Translationese has shown that, whereas humans cannot distinguish between translated and original text,computers have been trained to detect Translationesesuccessfully. It remains to be seen whether the same can be done for what we call Post-editese. We first establish whether humans are capable of distinguishing post-edited texts from human translations, and then establish whether it is possible to build a supervised machine-learning model that can distinguish between translated and post-edited text

    Reviews

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    Europe In the Round CD‐ROM, Guildford, Vocational Technologies, 1994

    The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models

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    Visual storytelling is an intriguing and complex task that only recently entered the research arena. In this work, we survey relevant work to date, and conduct a thorough error analysis of three very recent approaches to visual storytelling. We categorize and provide examples of common types of errors, and identify key shortcomings in current work. Finally, we make recommendations for addressing these limitations in the future.Comment: Accepted to the NAACL 2019 Workshop on Shortcomings in Vision and Language (SiVL

    Improving Content Area Reading in a Middle School Core Classroom

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    Many middle school students cannot adequately use content area textbooks. This project begins with a review of the research literature related to this concern. The literature review is followed by a content area reading program designed for an eighth grade core classroom. The program is composed of sequential directions and worksheets to teach diverse content area reading techniques. The relative values of different techniques are discussed, and suggestions for future studies are offered

    Analysis of the Correlation Between the Lexical Profile and Coh-Metrix 3.0 Text Easability and Readability Indices of the Korean CSAT From 1994–2022

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    The Korean College Scholastic Ability Test (CSAT) is a highly competitive standardized assessment that graduating high-school seniors complete in the hope of getting a good score which will improve their chances of admission to a university of choice. The CSAT contains an English Section that has been described by scholars and educators alike as being far too difficult for the official English language curriculum to serve as sufficient preparation. The test’s lack of construct validity has been the basis for calls to revise the test to be better reflective of the school curriculum so that it can serve the evaluative purpose for which it is intended. Use of automated text evaluation methods with the software Coh-Metrix 3.0 in recent years has allowed scholars to quantify different dimensions of the text of the CSAT English Section, such as cohesion and syntactic complexity, that contribute to its reading difficulty. Older research conducted before the introduction of this software into the field used word frequency counts in large corpora such as the British National Corpus (BNC) as a measure of word familiarity or unfamiliarity, which was thought to directly contribute to difficulty because as the proportion of low-frequency words in a text increases against the proportion of high-frequency words, the word knowledge burden of the text increases in proportion. Since the introduction of automated software-based tools like Coh-Metrix 3.0 and Lexical Complexity Analyzer (LCA), these corpus-based research methods have largely fallen by the wayside. In this paper, I maintain that despite its lower sophistication, corpus-based lexical analysis can still produce uniquely meaningful findings because of the degree of manual control the researcher is afforded in calibrating the parameters of the text base and, most importantly, in selecting the ranges of word family frequency that are best tailored to a text rather than having the ranges or functions of frequency assigned automatically by software. This study reports correlations between the outputs of these two methodologies that both inform us about the validity of Coh-Metrix 3.0’s use in CSAT studies and quantify the strength of the role of word frequency in causing the excessive difficulty of the CSAT English Section

    Semantic based Text Summarization for Single Document on Android Mobile Device

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    The explosion of information in the World Wide Web is overwhelming readers with limitless information. Large internet articles or journals are often cumbersome to read as well as comprehend. More often than not, readers are immersed in a pool of information with limited time to assimilate all of the articles. It leads to information overload whereby readers are trying to deal with more information than they can process. Hence, there is an apparent need for an automatic text summarizer as to produce summaries quicker than humans. The text summarization research on mobile platform has been inspired by the new paradigm shift in accessing information ubiquitously at anytime and anywhere on Smartphones or smart devices. In this research, a semantic and syntactic based summarization is implemented in a text summarizer to solve the overload problem whilst providing a more coherent summary. Additionally, WordNet is used as the lexical database to semantically extract the text document which provides a more efficient and accurate algorithm than the existing summary system. The objective of the paper is to integrate WordNet into the proposed system called TextSumIt which condenses lengthy documents into shorter summarized text that gives a higher readability to Android mobile users. The experimental results are done using recall, precision and F-Score to evaluate on the summary output, in comparison with the existing automated summarizer. Human-generated summaries from Document Understanding Conference (DUC) are taken as the reference summaries for the evaluation. The evaluation of experimental results shows satisfactory results
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