Science teacher training programme in rural schools: An ODL lesson from Zimbabwe
Bibliographic record
Abstract
<p>This case study looked at 76 randomly selected preservice science teachers from Mbire and Guruve districts who were learning at the Mushumbi Centre in Zimbabwe and assessed their motivations for enrolling under the Bindura University of Science Education (BUSE)’s Virtual and Open Distance Learning (VODL) programme. It also looked at the challenges they faced, their views on how instruction under the programme can be improved, and their deployment preferences after graduation. The districts are located in the remote Zambezi Valley, which is characterized by poor infrastructure, pests and diseases, frequent attacks by wild animals on people, domestic animals, and crops, harsh climatic conditions, and seasonal floods, which make it very difficult to attract and retain qualified teachers. Through targeted recruitment, BUSE’s VODL programme sought to train relief teachers already serving in the area in the hope that personal history and family connections would entice them to continue teaching in these areas after attaining their teacher certification. Data was collected using a questionnaire with closed and open-ended questions. Results obtained indicate that despite a lack of funding, a shortage of reading materials, and the nonavailability of e-learning facilities, the students were motivated to join the programme for personal and professional motives and that the students, the majority of whom had taught for two or more years in the districts, would prefer deployments in the area after graduation. The study therefore recommends that deliberate efforts be directed toward the targeted recruitment of school leavers and relief teachers from disadvantaged rural areas who possess the requisite minimum entry qualifications to train as science teachers in order to improve teacher retention in remote areas. Further research into the intrinsic problems in BUSE’s VODL programme and a close scrutiny of its course development techniques are also encouraged in order to overcome some of the problems encountered by both the students and the institution.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".