Are better communicators better readers? An exploration of the connections between narrative language and reading comprehension
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
MetadataShow full item record
AbstractThe association between receptive language skills and reading comprehension has been established in the research literature. Even when the importance of receptive skills for reading comprehension has been strongly supported, in practice lower levels of skills tend to go unnoticed in typically developing children. A potentially more visible modality of language, expressive skills using speech samples, has been rarely examined despite the longitudinal links between speech and later reading development, and the connections between language and reading impairments. Even fewer reading studies have examined expressive skills using a subgroup of speech samples – narrative samples – which are closer to the kind of language practitioners can observe in their classrooms, and are also a rich source of linguistic and discourse-level data in school-aged children. This thesis presents a study examining the relationship between expressive language skills in narrative samples and reading comprehension after the first two years of formal reading instruction, with considerable attention given to methodological and developmental issues. In order to address the main methodological issues surrounding the identification of the optimal linguistic indices in terms of reliability and the existence of developmental patterns, two studies of language development in oral narratives were carried out. The first of the narrative language studies drew data from an existing corpus, while the other analysed primary data, collected specifically for this purpose. Having identified the optimal narrative indices in two different samples, the main study examined the relationships between these expressive narrative measures along with receptive standardised measures, and reading comprehension in a monolingual sample of eighty 7- and 8-year-old children attending Year 3 in the UK. Both receptive and expressive oral language skills were assessed at three different levels: vocabulary, grammar and discourse. Regression analyses indicated that, when considering expressive narrative variables on their own, expressive grammar and vocabulary, in that order, contributed to explain over a fifth of reading comprehension variance in typically developing children. When controlling for receptive language however, expressive skills were not able to account for significant unique variance in the outcome measure. Nonetheless, mediation analyses revealed that receptive vocabulary and grammar played a mediating role in the relationship between expressive skills from narratives and reading comprehension. Results and further research directions are discussed in the context of this study’s methodological considerations.
PublisherUniversity of Wolverhampton
TypeThesis or dissertation
Showing items related by title, author, creator and subject.
Gender differentiation and the asymmetrical use of animate nouns in contemporary CzechDickins, Tom (Modern Humanities Research Association, 2001)This article analyses the use of animate nouns in contemporary Czech, with detailed reference to the dictionary Slovník spisovné etiny pro kolu a veejnost. Special attention is paid to the existence of generic masculine forms, which may underscore traditional perceptions of the status of men and women in Czech society. The study is informed by sociolinguistic theory and provides an overview of some of the relevant tenets of feminist argument, but it is primarily concerned with the linguistic implications of lexical practice. The main conclusion is that Czech is formally well adapted to suffixation and that there may now be scope for more feminine derivatives to assert themselves. (Ingenta)
Unity and Diversity within Pidginized Arabic as Produced by Asian Migrant Workers in the Arabian GulfAlbaqawi, Najah Salem (University of Central Lancashire 2010-2013, 2016-11)Gulf Pidgin Arabic (GPA) is a simplified contact variety of language spoken in the Gulf States in the Middle East. This unique linguistic phenomenon has resulted from the frequent language contact between the non-indigenous workforce with no Arabic skills, who come from countries such as India, Indonesia, Pakistan and the Philippines for job opportunities, and native speakers who do not share a common language with them. Pidgin languages have not been studied until relatively recently, since the middle of the last century. Similarly, GPA has received relatively little attention in the literature apart from a few descriptive works such as Albakrawi, 2013; Alghamdi, 2014; Almoaily, 2008, 2012; Alshammari, 2010; Al-Zubeiry, 2015; Avram, 2014, 2015; Gomaa, 2007; Hobrom, 1996; Næss, 2008; Smart, 1990; Wiswal, 2002. This study aims to propose an account of both unity and diversity within Asian migrant Arabic pidgins in the states of the Arabian Gulf in terms of a set of parameters where purely linguistic developments interact with contextual ones. The analysis of the social situation and of the available linguistic data shows that the main factor behind conventionalizing within GPA is migrants’ mobility in the Gulf region. This is basically compatible with Bizri (2014) who suggests that in Asian Migrant Arabic Pidgins (AMAP) “[’] mobility across the region is the major factor for homogenizing both native Arabic-speakers’ foreigner talk and migrants’ pidgin Arabic” (p. 385). One of the recommendations at the end of the study is that Saudi government should offer some courses for the foreign laborers to help them become familiar with basic Arabic words.
A MACHINE LEARNING APPROACH TO THE IDENTIFICATION OF TRANSLATIONAL LANGUAGE: AN INQUIRY INTO TRANSLATIONESE LEARNING MODELSMitkov, R., Corpas, G., Inkpen, D.; Ilisei, Iustina-Narcisa (University of Wolverhampton, 2012-10)In the eld of Descriptive Translation Studies, translationese refers to the speci c traits that characterise the language used in translations. While translationese has been often investigated to illustrate that translational language is di erent from non-translational language, scholars have also proposed a set of hypotheses which may characterise such di erences. In the quest for the validation of these hypotheses, embracing corpus-based techniques had a well-known impact in the domain, leading to several advances in the past twenty years. Despite extensive research, however, there are no universally recognised characteristics of translational language, nor universally recognised patterns likely to occur within translational language. This thesis addresses these issues, with a less used approach in the eld of Descriptive Translation Studies, by investigating the nature of translational language from a machine learning perspective. While the main focus is on analysing translationese, this thesis investigates two related sub-hypotheses: simpli cation and explicitation. To this end, a multilingual learning framework is designed and implemented for the identi cation of translational language. The framework is modelled as a categorisation task, the learning techniques having the major goal to automatically learn to distinguish between translated and non-translated texts. The second and third major goals of this research are the retrieval of the recurring patterns that are revealed in the process of solving the task of categorisation, as well as the ranking of the most in uential characteristics used to accomplish the learning task. These aims are ful lled by implementing a system that adopts the machine learning methodology proposed in this research. The learning framework proves to be an adaptable multilingual framework for the investigation of the nature of translational language, its adaptability being illustrated in this thesis by applying it to the investigation of two languages: Spanish and Romanian. In this thesis, di erent research scenarios and learning models are experimented with in order to assess to what extent translated texts can be di erentiated from non-translated texts in certain contexts. The ndings show that machine learning algorithms, aggregating a large set of potentially discriminative characteristics for translational language, are able to di erentiate translated texts from non-translated ones with high scores. The evaluation experiments report performance values such as accuracy, precision, recall, and F-measure on two datasets. The present research is situated at the con uence of three areas, more precisely: Descriptive Translation Studies, Machine Learning and Natural Language Processing, justifying the need to combine these elds for the investigation of translationese and translational hypotheses.