The Effect of Problem-based Learning on Critical Thinking Ability of Iranian EFL Students
Bibliographic record
Abstract
In order to make a change in the way students are prepared to meet the demands of the new century, new teaching methods are under investigation. Problem-based learning is one such method believed to encourage the skills students need to succeed. It was hypothesized that problem-based learning can have impact on critical thinking ability of Iranian EFL students. The data was collected from Iranian students at Dorsa language institute located in Hashtgerd, Alborz province. To homogenize the participants, the researcher had a language proficiency test (PET) as pre-test to 71 EFL learners. Out of 71 students, 40 students were selected. They were divided into one experimental and one control group based on their scores. The researcher used independent sample T–Test to measure the statistical differences between the two groups. Before receiving instruction the students completed a critical thinking questionnaire. After sixteen sessions of problem-based instruction, the researcher administrated the post-tests to both experimental and control groups. Finally, the results of the analysis of the data revealed that doing problembased activities enhanced critical thinking ability of the subjects. The findings of this study confirmed that participation in problem-based learning class had a significant effect on EFL learners‟ critical thinking ability.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".