Technical Oral Presentations (TOP) in EFL Engineering Education: A Systematic Literature Review on Workplace Communication Preparedness
Bibliographic record
Abstract
Workplace communication is a critical skill for engineering graduates, particularly those in English as a Foreign Language (EFL) contexts, where language proficiency can impact career success. This systematic literature review examines the effectiveness of Technical Oral Presentation (TOP) in enhancing workplace communication skills among EFL engineering undergraduates. The review synthesises recent studies (2015-2024) on TOP, focusing on its role in improving technical articulation, fluency, audience engagement, and professional presentation skills. There were a total of studies (n = 60) that were analysed. The analysis indicates that TOP fosters clarity in delivering complex engineering concepts, enhances confidence in spoken English, and develops essential soft skills such as teamwork and adaptability. Additionally, structured feedback and repeated practice contribute to the reduction of language-related psychological factors such as anxiety, lack of self-confidence and fear of making mistakes and speaking in public. However, challenges such as limited exposure to authentic workplace scenarios and language communication barriers persist. The study highlights the need for pedagogical frameworks integrating industry-relevant communication strategies with engineering curricula. This review concludes that while TOP is a valuable tool in preparing EFL engineering students for professional environments, its effectiveness depends on structured implementation, targeted feedback, and alignment with real-world communication demands. Future research should explore innovative approaches, such as digital tools and immersive learning, to further enhance TOP’s impact.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".