Enhancing Language Learning through PBL in an Aviation Engineering Class
Bibliographic record
Abstract
English for specific purposes is a field of teaching and learning that focuses on English language skills in context. It bridges the gap between general language knowledge and specific communication skills to enable students to meet the demands of their future professional field. For instance, aviation maintenance engineering requires the knowledge of highly specialized terminology. By providing appropriate and relevant linguistic tools, ESP enables aviation students to successfully perform job-related tasks and become more professionally competent within the aviation industry. While ESP has a role in improving language learning, the student’s experience can be further enhanced by incorporating Project-Based Learning into the curriculum to make learning more meaningful through inquiry-driven, task-based, and problem-solving paradigms. One key advantage of ESP in this regard is its adaptability, which means it can be adapted into a functional course. According to Dudley-Evans and St Johns (1998): “ESP was, for example, very influential in showing how a communicative language curriculum could be turned into either a functional-notional syllabus or a task-based syllabus” (Dudley-Evans & St Johns, 1998). The purpose of this article is to demonstrate a PBL method used in an ESP class aimed at aircraft maintenance engineers. The project involves students assembling a model aircraft jet engine. The goal of the project is to help students develop adequate knowledge about aircraft jet engines by acquainting them with the names and functions of the engine’s parts and also teaching them about its complex operation. Moreover, the project trains students on how to log their in-class activities into a weekly log that tracks their progress. At the end of the project, students reflect on their experience by completing a questionnaire that evaluates the outcomes of their learning. This helps the instructor assess the effectiveness of the project on the student’s language learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".