Knowledge Mapping of Critical Thinking in English Teaching Based on CNKI Scholar Using CiteSpace
Bibliographic record
Abstract
English, as one of the most widely used languages globally, serves as a crucial bridge in international economic, cultural and academic exchanges. In recent years, with the expansion of globalization, significant developments have been observed in publications in the field of English teaching, especially emphasizing critical thinking as a key skill for the 21st century. Therefore, a scientific review of the integration of critical thinking in English teaching is needed. This paper analyzes the scientometric features of critical thinking literature published in English teaching from 2020 to 2024. The research papers were sourced from CNKI Scholar, and CiteSpace was used to analyze 756 articles on English critical thinking pedagogy, presenting knowledge maps of authors, publishing institutions, keyword co-occurrence and cluster network analysis. Firstly, the result highlights that critical thinking, junior high school English, senior high school English, English reading teaching were among the most frequently occurring keywords in English critical thinking teaching research. Additionally, this review organizes the research hotspots and main contents in English critical thinking research, covering areas such as its application at various educational stages, content areas, and research perspectives of critical thinking in English teaching. Overall, the paper identifies key features, current trends, gaps, and potential future directions in critical thinking research within English teaching, offering valuable insights and guidance for advancing this field in China.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.074 | 0.074 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".