Challenges and Strategies for Teachers and Learners of English as a Second Language: The Case of an Urban Primary School in Kenya
Bibliographic record
Abstract
With over 40 spoken tongues in Kenya, English serves as a language of instruction in schools and is taught from the onset of schooling, making the language a significant factor in academic achievement and subsequent social mobility. This article draws on a case study conducted in an urban multilingual primary school in Kenya and focuses on the challenges and strategies for teaching and learning English as a second language (ESL) in primary schools. The findings are based on evidence gathered from teachers, through questionnaires and semi-structured interviews, and from pupils, through learner diaries. The data show a strategic approach to teaching and learning English and reveal the tremendous effort invested by teachers and learners in grappling with the challenges of learning English in the context of an unresolved national language policy, interference from regional linguistic heritage languages and an examination-oriented education system. The strategies deployed by teachers to address these challenges include varied instructional approaches and creating a warm classroom climate to provide a non-threatening environment for learning and language acquisition. Data from pupils shows that group based interactions with their peers and individual reinforcement strategies, such as keeping vocabulary notebooks, are the most common learner strategies. The study shows how school-based research can give teachers and learners a voice in the development of successful language teaching and learning strategies for complex and challenging multilingual environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".