Living up to Our Students’ Expectations – Using Student Voice to Influence the Way Academics Think about Their Undergraduates Learning and Their Own Teaching
Bibliographic record
Abstract
Understanding the student learning experience is essential if Higher Education Institutions (HEI) are to provide an education for the 21 st century. This study investigated students’ perspectives on their learning experiences and offered undergraduates a chance to influence the way academics think about learning and teaching. Participants were drawn from two UK HEIs and a semi structured focus group approach was adopted. A total of nine focus groups consisting of 3-7 participants were drawn from across all Sport degree year groups in both institutions. Assessment, pedagogy and teacher characteristics emerged as primary concerns across both institutions. Assessment was appreciated by all students as key to their learning but was exposed as being overly traditional and rigid in its application. Students were unanimous in their support for small group pedagogies, rejecting traditional powerpoint dominated lecturing styles. The emphasis on the behaviour of, and delivery by, tutors was noteworthy. Students appraised the development of their academic skills and confidence, linking these to motivation, knowledge, self-awareness and critical reflection. In doing so they understood the impact of inconsistencies in tutors’ teaching practices. The onus is on every tutor to combine imaginative assessment with dynamic and relational experiences in order to provide a strong foundation for flexible, reflective and creative graduates.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".