Conceptualising an inclusive approach to student voice in higher education
Bibliographic record
Abstract
Higher education seeks the student voice through various approaches. However, including students with diverse learner needs (DLN) in these approaches poses challenges when hierarchy is present. Through self-reflection and collaboration with co-researchers, I engaged in heuristic inquiry to develop a framework for engaging in an inclusive approach to gathering student voice. Nine co-researchers completed 14 individual conversational interviews with me to discuss their student voice experiences in higher education. Five key themes emerged: (1) needing a trauma-informed safe space to regulate emotions, (2) removing judgement through implementing trauma-informed practice, (3) embracing understanding and representation to enable authentic interaction, (4) removing fear by humanising those in positions of power, and (5) needing choice and autonomy. These themes demonstrated DLN students’ desire to cease masking and become more autonomous and authentic in their experiences, thereby moving toward partnership approaches. Drawing on these themes, I an inclusive student voice (ISV) approach and recommendations for practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".