A Rationale for the Integration of Critical Thinking Skills in EFL/ESL Instruction
Bibliographic record
Abstract
Critical thinking has become a high priority in almost every institution and educational system around the world, particularly since the second half of the 20th century. Developing the learners’ critical thinking skills has become an educational ideal that schools strive to achieve. There is a tacit consensus about the importance of incorporating critical thinking in education and ample literature has been written about it although there are different approaches to how this should be done. Integrating critical thinking skills in language instruction, however is a less explored area, especially when it comes to justifying this process. The main purpose of this paper is to present a rational for the inclusion of critical thinking skills in language teaching with reference to EFL and ESL. Five categories of reasons are suggested to support the implementation of critical thinking skills in the language classroom. The first is philosophical reasons related to the connection between language and thought. The second is cognitive and metacognitive reasons dealing with how critical thinking skills influence and are influenced by processes such as memory, comprehension and metacognition. The third is pedagogical reasons related the fact that many modern language teaching methods and techniques today require the learner to engage in problem solving, evaluation and decision making. The last are socio-economic reasons linked to the requirements of the job market.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".