An Examination of the Congruency between the University Teacher Training Contents and Secondary School Contents in Tanzania: The Case of Sokoine University of Agriculture
Bibliographic record
Abstract
<p>It is ideally expected that after student teachers have gone through comprehensive curricula contents, they should possess the necessary competences and skills to enable them deliver effectively as teachers. However, some student teachers in Tanzania have expressed their concerns that some of the contents found in the curricula for teacher training programmes do not link with the contents taught in secondary schools. Therefore, this study was designed to examine the congruency between the contents student teachers cover during their studies at Sokoine University of Agriculture (SUA), one of the Tanzanian teacher training institutions, and contents taught in Tanzanian secondary schools. The study involved a randomly obtained sample of 181 third year students, pursuing various teacher education degree programmes. The study adopted a cross-sectional research design in which a set of questionnaire, which consisted of both open and closed-ended questions, was administered to the sampled respondents. The collected data were analyzed using SPSS in which frequencies and percentages of responses to the questions presented in the questionnaire were computed to answer research questions advanced for this study. The findings were as follows: First, the majority (66.9%) of student teachers felt that there was a congruency between the University contents and secondary school contents and about one third (33.1%) of student teachers felt that the congruency did not exist. Secondly, the majority (95%) of student teachers felt that the contents of education courses taught at the University were applicable in real school situations and the minority (5%) did not feel so. Third, the majority (91.7%) of student teachers felt that the teacher training programmes offered at SUA had enabled them to acquire sufficient classroom teaching skills and basic theories in education. Fourth, some challenges facing teacher training programmes offered at SUA, as reported by the respondents, included irrelevant contents in some university courses, inadequate infrastructures, and lack of opportunities for practice, to mention but a few. Based on these findings, it is recommended that whenever an opportunity to review the existing teacher training programmes comes, the exercise should be preceded with needs analysis to help determine if the existing programmes adequately address the needs of the teaching profession for which the student teachers are being prepared.</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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".