An Exploration of Teaching Strategies Used to Teach Natural Sciences at the Science Centre in Pretoria, South Africa
Bibliographic record
Abstract
This study aimed to examine the teaching strategies employed to teach natural sciences at a science museum in Pretoria, South Africa. Science museums have collaborated with the Department of Basic Education to enhance the quality of science education in the country. A qualitative case study design was adopted to gather data from the science museum. Using a purposive sampling strategy, two education officers responsible for teaching and learning at the museum were selected as participants. Data collection methods included semi-structured interviews and observations. The findings revealed that education officers predominantly used a teacher-centred approach and a show-and-tell method to teach natural sciences. Additionally, lecturing and questioning formed a significant part of their instructional strategies. The study also noted the reliance on one-way communication methods, where learners were passive listeners and only engaged in conversation when prompted by questions. The article recommends training education officers to adopt facilitation roles and explore more effective teaching strategies. Such training could enhance the quality of natural sciences teaching and foster active engagement and deeper learning among learners.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".