A Corpus-based Study: A Comparison between China and Abroad in Current Hot Topics of English Language Teaching within Abstracts of TESOL Conventions
Bibliographic record
Abstract
The purpose of this study was to explore the current hot topics of English language teaching (ELT) between China and abroad using a corpus-based approach, with the research data taken from key words of 2775 abstracts within the 2021-2022 program books of International TESOL conferences and TESOL China assembly as four corpora, two of China and abroad in 2021 as well as two in 2022. It attempted to give significant proposals for future ELT research in China. A mixed methodology of (Quantitative +qualitative) was employed, one of which was AntConc software, for quantitative analysis on the data of high-frequency key words coded from each corpus. A qualitative analysis was subsequently conducted to analyze the potential reasons of major differences lie in Chinese and foreign hot topics of ELT. The study found major differences in terms of four aspects of hot topics of ELT between China and abroad: 1). Chinese academics mostly employed presentation as the primary teaching method, whereas foreign countries used a different variety of teaching methods, including online teaching tools, amount of teaching activities; 2). China stressed students’ summary writing and public speaking skills when it came to the aspect of student cultivation. On the other hand, foreign nations helped students improve their academic and reflective writing skills; 3). In China, test and peer assessment were the crucial methods of teaching evaluation, while formative assessment was used as the pivotal role in foreign countries; 4). China focused on the construction of teachers' professional identities and strategy training in teacher development. Yet, foreign countries concentrated on teachers' professional development. Pedagogical implications are that future ELT research in China needs to draw on overseas experience for drilling in four aspects: teaching methods, student cultivation, teaching evaluation and teacher development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".