Implicit Beliefs about English Language Competencies in the Context of Teaching and Learning in Higher Education: A Comparison of University Students and Lecturers in Namibia
Bibliographic record
Abstract
In many African countries, English is the medium of instruction in higher education even though students may not always be entirely familiar with “standard” English. This study aimed at investigating the relevance of English language competencies for teaching and learning from the perspective of students and lecturers. The study was carried out in Namibia and guided by the conceptual framework of implicit theories. Through a self-administered questionnaire, data were collected from a sample of 286 undergraduate students and 34 lecturers. Students and lecturers differed statistically significantly in all their views on the topic under investigation. While most of the lecturers (85.3%) believed that their students would not have good English language competencies, the majority of students (87.8%) rated their English between good and excellent. Most lecturers believed that insufficient English language competencies would cause a variety of problems for students such as having difficulty expressing themselves in English, following lectures, taking good notes during lectures, understanding academic texts, and writing coherent essays; in contrast, the majority of students believed that they had no such problems. The results are discussed with regard to practical implications for teaching and learning in higher education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".