The Communicative Ability of Universiti Teknologi MARA Sarawak’s Graduates
Bibliographic record
Abstract
This study explores Universiti Teknologi MARA (UiTM) Sarawak graduating students’ oral proficiency, focusing on grammatical accuracy. Oral proficiency in English has always been the benchmark of language proficiency, and in the context of UiTM’s language teaching curriculum, efforts to enhance students’ oral proficiency are implemented through various languages proficiency-based and ESP courses. A small corpus of group discussions was analysed to identify and classify grammatical errors and other performance factors in students’ oral communication. The analysis was carried out using text analysis software used in corpus linguistics (Dagneaux et al, 1998, Scott, 1996). The software was used to classify grammatical errors into general categories such as grammar, word, lexical, formal, style, lexico grammar and register. It was found that the students who took part in group discussions displayed frequent lexical, grammatical and formal errors. Also there was much evidence of performance phenomena such as hesitation, repetitions, incomplete structures and redundancy. This provides evidence that, despite many years of ESL input, some UiTM graduating students still seem to require a great deal of practice with basic grammatical structures. The practical implications derived from the findings of the study suggest that teachers and curriculum developers ought to help students develop their proficiency in English language learning by giving them ample opportunity to use, produce and practice English language structures or sentences through simulated situations in various immersion activities in class and outside the classroom as well as introducing consciousness-raising techniques to sensitise learners to the various forms and meanings of structures used in everyday conversation in English.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".