Computational Text Analysis and Reading Comprehension Exam by Trisevgeni Liontou

By Trisevgeni Liontou

This e-book delineates quite a number linguistic beneficial properties that characterise the interpreting texts used on the B2 (Independent consumer) and C1 (Proficient consumer) degrees of the Greek nation certificates of English Language skillability checks to be able to aid outline textual content hassle according to point of competence. moreover, it examines no matter if particular reader variables impression attempt takers’ perceptions of studying comprehension hassle. the tip product is a textual content category Profile in keeping with point of competence and a formulation for immediately estimating textual content trouble and assigning degrees to texts continuously and reliably in response to the needs of the examination and its candidature-specific features.

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Extra resources for Computational Text Analysis and Reading Comprehension Exam Complexity: Towards Automatic Text Classification

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For example, in an attempt to explore the impact of maintaining L1 reading skills on L2 reading skills development in adults, Pichette et al. (2003: 393) selected texts from a French journal and estimated their readability using Henry’s readability formula for French, which assigns scores to texts based on such factors as the number of words per sentence, the percentage of words that belong to a list of common words and the percentage of words that serve as dialogue indicators. Parker et al. (2001: 307) investigated the correlation between two Spanish readability formulas and Spanish-speaking second graders’ reading performance in nine elementary-level stories.

2002: 127; Spadorcia, 2005: 37). , 2001: 308; Shokrpour, 2004: 15–16). According to Carrell (1987a: 34), what is needed is a clearer theoretical approach to readability, one which takes a broader range of reader as well as text variables into consideration. As Kintsch and Vipond pointed out, the problem of predicting reader comprehension “is not with the formula but with our theories” (1979: 335). , 2001: 308; Pichert & Elam, 1985: 184). Finally, within the context of language testing, Bailin & Grafstein (2001: 299) stressed that “this is a particularly propitious moment to re-examine the use of readability formulas as a measure of reading difficulty”.

They also found that their subjects’ ratings regarding text comprehension were meaningful and were in fact significant predictors of their comprehension scores throughout the texts (ibid: 1243). Similar results were obtained by Ozuru et al. (2005: 1699), who used Coh-Metrix to explore the effect of text cohesion on reading comprehension of challenging science texts among students with little topicrelevant knowledge. In that study, introductory level psychology students read high and low cohesion versions of biology texts.

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