Challences Facing Tutors in the Teaching of Visual Arts Education in National Teacher Colleges in Uganda
Bibliographic record
Abstract
The study examined the challenges faced by tutors in the teaching of the visual arts education (VAE) in national teachers’ colleges (NTCs) in Uganda. The study adopted a qualitative approach where tutors’ and pre-service visual arts teachers’ (PVATs) views about the challenges facing them in the teaching and learning in visual arts were expressed. Data were collected from two purposively selected NTCs, and ten tutors. Yet, the 48 second year PVATs who participated in this study, were randomly selected from the many who were available. The researchers used interviews, document reviews and focus group discussions to collect data. The findings show that the challenges facing tutors in the teaching of visual arts have a great impact on what PVATs learn. Some visual art disciplines have too much content to be covered within a short period of two years. There is a general lack of teaching resources, such as art materials, tools and equipment, textbooks, and inadequate teaching space. The researchers recommended the reduction of the content of some visual art disciplines to fit the available time; provide art materials, tools and equipment as well as adequate teaching space which would allow the use of more appropriate teaching methods which would avail tutors with the opportunity to perform to their expectations in visual arts teaching.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".