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The Effects Of Depth Of Vocabulary Knowledge And Passage Sight Vocabulary On English Majors’ Lexical Inference Strategy Through Reading Comprehension

Posted on:2019-06-24Degree:MasterType:Thesis
Country:ChinaCandidate:D YongFull Text:PDF
GTID:2405330545983927Subject:Curriculum and pedagogy
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Inferencing named as the core of reading comprehension is one of the key cognitive processes in discourse reading.Lexical inferencing is a cognitive strategy for the readers to get access to the meaning of unknown words in the text by making a comprehensive use of contextual clues or background knowledge.It is also of great importance in the development of readers’ vocabulary and textual comprehension,therefore,it has drawn much attention from researchers at home and abroad.In the previous studies on L2 vocabulary acquisition,most researchers tend to focus on the relationship between depth of vocabulary knowledge(DVK)and reading comprehension and the relationship between passage sight vocabulary(PSV)and vocabulary acquisition,only a small part of the studies refer to the relationship among DVK,PSV and inference strategies use and success.Based on Rieder’s(2004)“Focus” and “Enrichment”model,Huckin and Bloch’s(1993)Cognitive processing model of L2 lexical inferencing and Graesse,Singer and Trabasso’s(1994)constructionist theory,this study carries out an experiment to investigate the relationship among DVK,PSV and vocabulary inferencing strategy use and success.To put it more specifically,this study is designed to answer the following four questions:(1)What inferential strategies do English majors use when attempting to infer words’ meanings from context? And how successfully do they infer the words’ meanings?(2)What is the relationship between learners’ depth of vocabulary knowledge and lexical inference strategy use and success?(3)What is the relationship between learners’ passage sight vocabulary and lexical inference strategy use and success? 30 English majors of a college in Gansu province were chosen as subjects.Firstly,the Word-Associate Test and Passage Sight Vocabulary Test were conducted to test learners’ depth of vocabulary knowledge and their passage sight vocabulary size respectively.Then the participants were asked to read the passage containing 8 target words and attempt to infer the meaning of these unknown words from the context.At the same time,the think-aloud protocol was used to discover their inference strategy use and success.The results of DVK and PSVT were collected and the think-aloud protocols were transcribed,which were then processed by SPSS18.0 software through the chi-square test,independent samples T-test and Pearson correlation analysis.The following conclusions have been reached after the analysis: firstly,the learners used the meaning-focused strategy most frequently and lexical skilled readers used making inquiry and commentingmore frequently than the less skilled ones;Secondly,the students who were more proficient in terms of depth of vocabulary knowledge made more frequent and effective use of evaluative and context-based strategies,including making inquiry and commenting strategy.The result also indicates that the students who are more proficient in terms of depth of vocabulary knowledge make more higher lexical inferencing success;Lastly,there are positive moderate significant correlations between the passage sight vocabulary size and lexical inferencing success,which means that the larger the passage sight vocabulary size is,the higher the lexical inferencing success will be.And the readers who have lager sight vocabularies have more efficient decoding skills so that they can allocate attentional resources to construct and integrate the ideas from context.Finally,the article puts forward the weak points of the experiment both in the theoretical and practical aspects.At the same time,some suggestions are proposed for future researches on vocabulary acquisition.
Keywords/Search Tags:The depth of vocabulary knowledge, Passage sight vocabulary, Lexical inferencing strategy, Reading comprehension
PDF Full Text Request
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