Attitude of Postgraduate Diploma in Teaching (PGDT) Trainees towards the Teaching Profession in Ethiopia
Bibliographic record
Abstract
This study explored the attitude of trainees in postgraduate diploma in teaching towards the teaching profession. The study had a descriptive survey design. Two hundred and fifty-eight trainees participated in the study. Proportionate stratified random and simple random sampling were used in the selection of the study participants. Quantitative data was collected using a standardized questionnaire developed by Renthlei and Malsawmi (2015). Percentages and Stanine scale frequency distributions were used to analyze the data and report the findings. The study revealed that a lack of other better job opportunities was the main reason for the trainees to choose teaching as their future profession. Most also expressed the interest to quit the training and join any other better job opportunity the moment they get the chance. Overall, the majority had a low attitude towards teaching as a profession. Such a low trainees’ attitude towards teaching and their intention to quit the training or leave the profession any time the opportunity comes their way, puts the government’s plan to expand the coverage of education in jeopardy. Clearly, an increase in schools cannot be achieved without a parallel increase in the number of qualified teachers. Finally, implications of the findings were forwarded to pertinent bodies in teacher education programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".