Challenges of virtual and open distance science teacher education in Zimbabwe
Bibliographic record
Abstract
This paper reports on a study of the implementation of science teacher education through virtual and open distance learning in the Mashonaland Central Province, Zimbabwe. The study provides insight into challenges faced by students and lecturers on inception of the program at four centres. Data was collected from completed evaluation survey forms of forty-two lecturers who were directly involved at the launch of the program and in-depth interviews. Qualitative data analysis revealed that the programme faces potential threat from centre-, institution-, lecturer-, and student-related factors. These include limited resources, large classes, inadequate expertise in open and distance education, inappropriate science teacher education qualifications, implementer conflict of interest in program participation, students’ low self-esteem, lack of awareness of quality parameters of delivery systems among staff, and lack of standard criteria to measure the quality of services. The paper recommends that issues raised be addressed in order to produce quality teachers.
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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 | 0.017 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".