A Corpus-Based Analysis of Text Complexity in NMET Reading and Its Implication on Reading Instruction
| Autor: | Xiaochen Hua, Liyan Huang, Jiaying Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Vocabulary media_common.quotation_subject 05 social sciences 050301 education Lexical diversity Syntax Linguistics Readability Test (assessment) Cohesion (linguistics) Reading (process) 0501 psychology and cognitive sciences Coh-Metrix Psychology 0503 education media_common |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789813345935 |
| DOI: | 10.1007/978-981-33-4594-2_3 |
| Popis: | This study builds a small-scale corpus of 54 reading texts of the National Matriculation English Test from 2016 to 2019 and uses natural language processing tools, Python and Coh-Metrix, to investigate text complexity with 35 quantitative indices from five aspects, including length, readability, vocabulary, syntax and cohesion. Results reveal the major characteristics of these texts, showing they have appropriate length, similar difficulty as the texts in high school English textbooks and a higher level of lexical diversity and stem overlap among all sentences than those in textbooks. The findings offer support for more precise and effective reading instructional practice in high school from the perspectives of the WHAT and the HOW in classroom teaching. |
| Databáze: | OpenAIRE |
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