A Systematic Literature Review on Enhancing Critical Thinking Skills in EFL Reading
Bibliographic record
Abstract
Critical thinking (CT) is crucial in teaching reading in English as a Foreign Language (EFL). Nonetheless, there remains a dearth of comprehensive research and insight into applying CT taxonomies, successful pedagogical practices, and teacher challenges in improving CT skills in EFL reading. This systematic literature review examines effective CT taxonomies, pedagogical strategies, and challenges in improving CT skills within EFL reading instruction. It synthesizes findings from 11 empirical studies published between 2014 and 2024, selected from Scopus and Web of Science databases using the PRISMA framework along with predefined inclusion and exclusion criteria. The findings reveal that Bloom’s taxonomy is the predominant framework alongside three significant teaching approaches in cultivating CT skills: traditional, constructivist, and technology-enhanced. Traditional approaches typically rely on teacher-centred instruction, including grammar teaching, text analysis, and comprehension exercises, which prioritize factual recall rather than fostering deep analytical thinking. The constructivist approach includes project-based learning, survey-question-read-recite-review, question-answer-relationships, and the exposure-exploration-evaluation model. The technology-enhanced approach, including online classes, flipped classroom teaching, and asynchronous web-based collaboration, has effectively cultivated CT skills. Despite the increasing integration of CT-based instructional frameworks and strategies, challenges remain in educators’ pedagogical competence, instructional methodologies, teaching resources, assessment instruments, and professional development. This study points out that it takes more research to create context-specific CT pedagogical models, enhance teacher training programs, and refine assessment frameworks to successfully integrate CT skills into EFL reading instruction.
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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".