Students’ perception of conditions for self-efficacy during their internship programme. A case study of the University of Education, Winneba College for Distance And e-Learning
Bibliographic record
Abstract
This article examines the conditions (time and supervision) in achieving self-efficacy among distance education students on internship programme in the University of Education, Winneba. The purpose of this study was to find out if the conditions of time and supervision were adequately met for distance education students on internship programme in the University of Education, Winneba (UEW) to accomplish self-efficacy. The descriptive survey design was used in conducting the study. Data was collected by means of Google Docs; questionnaires were administered via students’ WhatsApp platforms. A sample of 1,087 final year distance education students was derived for the study through the availability sampling technique. The study concluded that the condition of time allocation for OCTPs to enable UEWDESTIs to achieve self-efficacy were not adequately met in the sense that they had little time to learn more relevant teaching skills, practicalise teaching theories, participate in every activity, and practice all teaching activities learnt. In essence, UEWDESTIs would need more time to practice and develop the teaching skills necessary to become more competent teachers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".