Measuring and comparing the readability and vocabulary coverage of CNN, China Post and Taipei Times

This study measured and compared the vocabulary coverage, vocabulary levels and readability of CNN, the China Post and the Taipei Times. It is hoped that this study could provide a more effective and cost-benefit way to assist teachers in choosing suitable texts for vocabulary learning and for reading instruction. It could also offer some information for material developers and programme administrators.<br/><br/>The results showed that the three online newspapers, CNN, the China Post and the Taipei Times had a GSL coverage of 77.85%, 7627% and 75.76% respectively and a AWL coverage of 5.54%, 5.83% and 5.57% respectively. Furthermore, the different coverage percentage of the 1st 1,000 GSL, 2nd 1,000 GSL and AWL in each news classification demonstrated its vocabulary focus. Implications for vocabulary course design with the word lists of the 1st 1,000 GSL, 2nd 1,000 GSL and the AWL based on the different news classifications could be applied.<br/><br/>From the results of the vocabulary levels examined by BNC word lists, the vocabulary levels of the different news classifications spread from 4,000 words to over 14,000 words. The sequencing of the vocabulary levels of the news classifications provides good reading sources of extensive reading and narrowing reading materials.<br/><br/>The reliability and validity tests showed that the Flesch Reading Ease, the Flesch-Kincaid Grade Level and the Fry Readability; Graph are reliable and valid tools in measuring readability of the news articles.The readability levels of the different news classifications were divided into grades 9 to 14. Although the different news classifications were graded into different levels, teachers still need to consider other factors that are not measured by the readability formulae when selecting the articles. Also, the readability formulae scores reflect the difficulty of the texts and it is not appropriate to apply those directly into the context in Taiwan.

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

Queen's University Belfast

Year dc:date.issued

2011

Author dc:creator

• Huang, Kai-Ting

Language dc:language

eng

Identifier

oai:pure.qub.ac.uk/portal:studenttheses/43057691-62ae-4f35-a8d8-8c65a5e7d1ca

OAI identifier oai:identifier

oai:pure.qub.ac.uk/portal:studenttheses/43057691-62ae-4f35-a8d8-8c65a5e7d1ca