The Effect of Project-Based Learning on First-Year Undergraduate Students in English for Specific Purposes (ESP) Courses
Bibliographic record
Abstract
Project-Based Learning is an approach that is significantly different from the conventional English Language Teaching method. PBL has been advocated as an effective instruction that promotes the simultaneous acquisition of language, content, and 21st-century skills to EFL (English as a foreign language) and ESL context. In this study researcher’s attempt is to integrate PBL (Project-Based Learning) approach, which incorporates authentic, learning by doing, problem-solving, critical thinking and team working skills with ESP (English for specific purposes) courses. The study aimed to investigate whether the PBL projects assist EFL learners in acquiring department related literature and information in the English language and the effect of project work on learners’ ESP course academic achievement. The study was conducted at Tishk International University, formerly known as Ishik University, in Iraq-Kurdistan Region with 2 Engineering (Architectural and Civil engineering) and Business and Management departments’ students. A mixed-method approach was designed to make the authentic interpretation of gained results. Quantitative and Qualitative instruments were utilized to probe the effect of PBL in the ESP course of undergraduate Iraqi EFL learners. The findings of this study reveal that PBL had a significant positive effect on acquiring department-related vocabulary and information in English as well as learners’ ESP course academic achievement showed statistically significant increment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".