Participation of Student Teachers in Decision-Making across Volta Region Colleges of Education in Ghana
Bibliographic record
Abstract
Student participation in higher education governance is internationally acknowledged as a core element of democratic practice and a mechanism for strengthening institutional accountability. However, the degree and effectiveness of such participation differ markedly across contexts. This study examines the extent of student teachers’ involvement in governance and the systemic constraints that shape their engagement across five Colleges of Education in the Volta Region of Ghana. Using a descriptive survey design, 361 student teachers were selected through stratified random sampling. Findings reveal limited awareness of governance structures among students and a narrow focus on Students’ Representative Council (SRC) roles in academic matters, with minimal influence on administrative decision-making. Centralised authority structures, weak communication systems, and inadequate dissemination of governance-related information contribute to persistent feelings of marginalisation and disengagement. The study underscores the urgent need for governance reforms aligned with global calls for participatory, transparent, and inclusive decision-making in higher education. It recommends institutionalising regular governance orientation and sensitisation programmes, strengthening formal communication channels, and enhancing SRC autonomy through improved representation, resourcing, and targeted leadership capacity-building. These interventions can promote more responsive, student-centred governance and contribute to broader international agendas for inclusive higher education systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".