Data on pre-service teachers’ experience of project activities based on the teacher education support project in Tanzania
Bibliographic record
Abstract
Supporting teacher education in Tanzania has long been a common practice implemented by both local institutions and development partners. Despite a huge investment that has been dedicated to improve teacher education in Tanzania, a lot remains unclear on how direct beneficiaries perceive their engagement with the project activities including the milestones achieved by the implemented projects in teacher colleges (TCs). This article presents data on the experience of pre-service teachers (N = 2,772) participating in the Teacher Education Support Project (TESP), a project collaboratively implemented by the Governments of Tanzania and Canada. In this cross-sectional survey, data was collected from all the 35 public TCs in the Tanzania Mainland from May to August 2021. Exploratory factor analysis was conducted coupled with Monte-Carlo parallel analysis to examine the factor structure of the questionnaire alongside the descriptive analysis of pre-service teachers’ responses. The data covers four dimensions of the project services delivered to TCs, including library facilities, teaching and learning materials, science and ICT support as well as teaching and learning methods employed by tutors following TESP intervention. Broadly, useful insights that enlighten the progress made by the TESP so far are presented to stimulate the debate on how to successfully implement a development project geared towards strengthening teacher education in Tanzania and elsewhere. The presented data provides opportunity for educational researchers, teacher educators, policymakers, and curriculum developers to rethink on the key areas that need immediate attention to enhance the important work of preparing teachers in TCs in Tanzania and possibly beyond.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".