An Analysis of Text Difficulty across Grades in Korean Middle School English Textbooks Using Coh-Metrix

Ryu, Jisu; Jeon, Moongee

doi:10.18823/asiatefl.2020.17.3.11.921

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…These findings were confirmed by some previous studies which revealed that teacher talk addressed to the lower proficiency learners consists of shorter sentences than that to those of higher proficiency. Ryu and Jeon (2020) revealed that sentence length is an indicator of sentence difficulty. Long sentences contain more complicated concepts and ideas, thus, becoming complex and more difficult to perceive (Zipoli, 2017).…”

Section: Findings and Discussionmentioning

confidence: 99%

Teacher Talk to Accommodate Low-Proficiency Learners in EFL Classes: A Case Study

Milal¹

2021

The Journal of AsiaTEFL

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Instructional design is usually focused on the presentation of materials. The principles of teachers' language-use strategies have scarcely been discussed. In an EFL context, teachers are required to use the target language to promote learners' language acquisition. What is the language like when the learners still have low language proficiency? This study aims to describe the speech features of nonnative teachers of English addressed to lower-proficiency learners in EFL classes. The data were collected by observation, interview, and recording the teachers teaching English in lower classes, i.e. semester one, and its comparable counterpart of higher classes, i.e. semester five of the English Department in a public university in Malang, Indonesia, and then analyzed by describing and comparing inter-levels intra-subjects. Despite some variability among subjects, it was found that there was a tendency that the language addressed to lower-proficiency learners has specific characteristics encompassing formal, interactional, and native language features that were simpler than that to higherproficiency learners. This study concludes a principle of instructional delivery that student's level of knowledge and language ability determined the level of teacher speech. Hence, instructional designers should also describe how a medium of instruction is used, not merely how the learning material is presented.

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Section: Findings and Discussionmentioning

confidence: 99%

Teacher Talk to Accommodate Low-Proficiency Learners in EFL Classes: A Case Study

Milal¹

2021

The Journal of AsiaTEFL

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“…The value of Coh-Metrix has been demonstrated in more than 50 published research studies (e.g., Crossley, Greenfield & McNamara, 2008;Crossley, Salsbury & McNamara, 2009;Duran, McCarthy, Graesser & McNamara, 2006;Kim & Lim, 2019;Ryu & Jeon, 2020). For example, studies using Coh-Metrix have been able to distinguish between highcohesion and low cohesion texts (McNamara, Ozuru, Graesser & Louwerse, 2006), analyze authentic and simplifies texts for L2 material development (Crossley, Louwerse, McCarthy & McNamara, 2007), and evaluate intelligent tutoring system dialogues (Wolfe, Widmer, Torrese & Dandignac, 2018).…”

Section: Coh-metrixmentioning

confidence: 97%

Counter-arguments and support arguments in student writing

2022

pegegog

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This study analyzes the linguistic features of counter-arguments and support arguments using two computational linguistic tools: Coh-Metrix and Gramulator. The research question investigates whether counter-argument paragraphs and support paragraphs are different in terms of their linguistic features. To conduct this study, a corpus of 78 argumentative papers was collected. The paragraphs in the papers were categorized in terms of their function. The categories included functions of Support, Counter-argument, Expostulation, Counter-argument and Expostulation, Background, and Other. The paragraphs were analyzed for their readability and writing quality through Coh-Metrix. With the exception of the measure of Deep Cohesion, the Coh-Metrix results suggest minimal differences in terms of readability and writing quality between counter-argument and support paragraphs. Following the Coh-Metrix analysis, both counter-argument and support corpora were analyzed through Gramulator for their lexical features. The Gramulator results suggest the presence of causal language and fixed expressions in counterarguments, as well as some tagged language in support arguments.

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“…Oveshkova [5] developed a task and activity system based on the English corpus and took the students of a foreign language college as the experimental object to observe the effect of the language learning method based on the corpus [5]. Ryu et al [6] investigated the textual difficulty of reading materials of English textbooks in Korean middle schools and analyzed the linguistic and psycholinguistic characteristics of English texts and textbooks by constructing an English reading corpus [6]. From the above studies, we can see that the current research direction of the English reading corpus is basically the research on its function, and the basic framework of the English reading corpus, such as vocabulary and word attributes, is rarely researched and analyzed.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis and Optimization of Feature Extraction-Oriented English Reading Corpus

2022

Mathematical Problems in Engineering

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English is a universal language in the world. It has become the consensus of society as a subject of education in primary and secondary schools and even universities. Therefore, how to improve English reading ability has also become a focus area of school education and students. The current research on English reading is mainly based on the sense of reading questions, reading patterns, answering skills, etc. and lacks the analysis of English reading corpus. In view of this, this paper used a self-built English reading corpus, adopts the feature extraction method, and combines the convolutional neural network (CNN) to build a model to carry out numerical analysis on the self-built English reading corpus, optimized the model, and compared and analyzed the results obtained. The optimal dropout rate and iteration times were obtained by updating experimental parameters. In order to make the experimental results more convincing, the W2V-SVM and W2V-CNN models that combine different feature extraction and classification methods are designed. Compared with the optimized CNN model, the accuracy rate, recall rate, and F1 value of the optimized CNN model were 89.81%, 92.39%, and 92.8%, respectively. The accuracy, recall, and F1 value of the W2V-SVM model are 81.31%, 82.09%, and 81.25%, respectively. The accuracy, recall, and F1 value of the W2V-CNN model are 85.24%, 84.98%, and 85.12%, respectively. It shows that the optimized CNN feature classification model has better feature classification effect on the self-built English reading corpus. The experimental results meet the expected value.

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An Analysis of Text Difficulty across Grades in Korean Middle School English Textbooks Using Coh-Metrix

Cited by 6 publications

References 30 publications

Teacher Talk to Accommodate Low-Proficiency Learners in EFL Classes: A Case Study

Teacher Talk to Accommodate Low-Proficiency Learners in EFL Classes: A Case Study

Counter-arguments and support arguments in student writing

Numerical Analysis and Optimization of Feature Extraction-Oriented English Reading Corpus

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