English Language Teaching in Thailand from the Perspective of Bangkok Post: A Corpus-assisted Analysis

Abstract

The main goal of the study is to investigate the content of news reports on English Language Teaching (ELT) as published by the Bangkok Post Online. Informed by a corpus based investigation of data, a total of 156 news reports on English Language Teaching (ELT), composed of 12187-word types and 209357-word tokens, were downloaded and prepared to be examined using the corpus linguistics software, AntConc 3.5.8 (Anthony, 2019). The findings revealed 71 keywords with log-likelihood values of at least 100 that were dominated by words relating to ELT and education matters (f=31; 43.66%), setting (f=11; 15.49%), ELT and education stakeholders (f=9; 12.68%), ELT and education physical structures and facilities (f=8; 11.27%), government concerns, offices and officials (f=6; 8.45%), production of language (f=5; 7.04%), and financial matters (f=1; 1.41%). These keywords depict the kinds of information presented by the reporters of Bangkok Post Online to their assumed readers. Implications on the use of corpus-assisted analysis in the investigation of ELT big data harnessed online were drawn.