Evaluation of the Vocational Education Orientation Programme (VEOP) at a university in South Africa
Bibliographic record
Abstract
<p>To address the training needs of Further Education and Training college (FETC) lecturers, and in the absence of a full professional education qualification, several higher education institutions, FETCs, and other bodies in South Africa formed an alliance to develop a short programme towards a possible future full qualification. In 2010 a Vocational Education Orientation Programme (VEOP) was piloted. In line with the responsibility for quality assurance, and the need to inform further developments in the training of FETC lecturers, the aim of this research was to evaluate the VEOP presented by the University of the Free State (UFS). To reach the stated aim, a two phase evaluative study was undertaken (1) to assess the individual modules, and (2) to holistically investigate the quality of the programme. Two questionnaires were used to gather data. The first set of data was collected at the completion of each of the six modules. For the second phase of the study, 48 lecturer-students were randomly selected more than a year after completion of the VEOP. The study identified a number of strengths and weaknesses of the VEOP. The results emphasise the need to carefully select tutors and train them to have an understanding of the FETC milieu, rethink the methodology employed in the education training of FETC lecturers, and redesign the modules’ contents to better reflect the FETC sector. The need to enhance student support and improve administration is also highlighted by the study. The results of the study may inform the development of a full qualification for FETC lecturers.</p>
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".