Pedagogical Strategies Employed by Teachers in Township Schools for Teaching Meiosis and Genetics with Improvised Resources
Bibliographic record
Abstract
This study explored the pedagogical strategies employed by grade 12, life-sciences teachers in township schools to teach complex concepts, such as genetics and meiosis, using improvised teaching resources. Resource constraints in South African township schools often limit learners’ access to traditional teaching materials and technologies. In response, this research examined how teachers adapt and innovate their methods to effectively convey abstract life-sciences concepts. An embedded mixed-methods design was utilized, with a purposive sample of four life-sciences teachers from diverse township schools, selected to reflect varied teaching experiences and resource availability. Data was collected through interviews and classroom observations, offering insights into their instructional practices. Thematic analysis of interview data and systematic observation of classroom activities revealed a range of creative and adaptive pedagogical approaches. Instructors commonly adopted collaborative, learner-centred, and inquiry-based teaching methods. They employed creative strategies, including designing hands-on activities, using analogies, and incorporating real-life examples to enhance learners’ understanding. Collaboration among teachers and the use of community resources also emerged as key strategies for enriching the learning experience. The findings underscore the resilience and ingenuity of grade 12, life-sciences teachers in overcoming resource constraints to create effective educational environments. This study contributes to the understanding of the interplay between pedagogy and resource availability in underserved educational settings, providing valuable insights for educators, policymakers, and curriculum developers aiming to enhance science education in resource-limited contexts.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".