Teaching in English across the Curriculum: A Lived Experiences of the Novice Teachers in A Selected Rural Fet Schools in South Africa
Bibliographic record
Abstract
Novice teachers’ use of English as the medium of instruction in curriculum delivery across all subjects in rural South African schools is the focus of this study. The duration of their entry into the profession as Post Graduate Certificate in Education holders is short and does not capacitate them enough, thus, making them weak and inexperienced, with a high degree of professionalism, especially at the commencement of their careers. This study investigates the lived experiences of selected novice rural teachers on the tasks of teaching in English across all subjects in the rural schools. Fifteen participants from 15 high schools were purposively selected from 15 different rural high schools from King Cetshwayo District, KwaZulu-Natal province in South Africa. The participants were engaged in a focus group discussion as well as classroom observations of these teachers for data collection. Data analysis for this qualitative study was thematically analysed to generate themes for the presentation and discussion of findings. Theories of the Skills Acquisition Theory (SAT) and the Second Language (L2) Comprehensible Input through Teacher Talk Theory (CITTT) were adopted as theoretical frameworks to underpin the phenomenon. Various novice teachers find it difficult to teach across all subjects, using English as the medium of instruction in many rural schools in South Africa. Findings indicated that the educational backgrounds of learners hinder teachers’ use of English to teach them meaningfully. As observed during various classroom observations, teachers also found it convenient to use indigenous languages to deliver lesson content or communicate to these learners in the classrooms during lessons. Findings also revealed that some teachers struggled to express themselves in English due to their educational backgrounds as well. The study, therefore, recommends that teachers during their pre-service education should be well-groomed in English and this should be promoted during their teaching practice in both rural and urban. Various teacher education institutions should be encouraged to deliver curriculum contents/modules to the pre-service teachers in English across all modules.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".