Perceptions of Jordanian Secondary Students on the Use of Problem-Based Learning Strategy in English Classes
Bibliographic record
Abstract
This study investigated secondary school students’ perceptions of the implementation of the problem-based learning (PBL) strategy in English language instruction in Amman, Jordan. Quantitative research design was employed, alongside a descriptive-analytical approach. Data was collected through a researcher-developed questionnaire based on a five-point Likert scale. A purposive sample of 486 male and female eleventh-grade students was selected from three public and three private secondary schools in Amman. The questionnaires were distributed manually during the second semester of the 2024–2025 academic year, and all responses were successfully collected for statistical analysis using SPSS. The findings revealed that students generally held positive perceptions of the use of PBL in the English language course. Moreover, the strategy was perceived as significantly contributing to the development of students’ core language skills—listening, speaking, reading, and writing—as well as enhancing their abilities in delivering presentations, engaging in effective communication, conducting inquiries, and articulating personal opinions. Based on these results, the study recommends offering targeted training programs for English language teachers in both public and private schools across Jordan, focusing on PBL pedagogy and promoting broader access to such instructional strategies through free professional development opportunities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".