Pedagogic partnership in higher education: encountering emotion in learning and enhancing student wellbeing
Bibliographic record
Abstract
Despite emotion being recognized as fundamental to learning, the affective aspects of learning have often been side-lined in higher education. In the context of rising student wellbeing challenges, exploring ways of supporting students and their emotions in learning is increasingly significant. Pedagogic partnerships have the potential to help students to recognize and work with their emotions in their learning in a positive manner. As such, pedagogic partnerships offer opportunities to promote resilience and enhance student wellbeing. In this paper, we develop partnership research in three ways by: 1) considering the ways in which pedagogic partnership may support students to encounter emotions and empower them to develop resilience, leading to positive wellbeing; 2) exploring how this process might be achieved in the disciplinary context of geography; and 3) developing an evidence-based model to summarize the potential effect of pedagogic partnership in enhancing student wellbeing. We draw upon two case studies of student-faculty and student-student pedagogic partnership within geography curricula in order to evidence that emotional awareness in learning comes through the joys and struggles of working in partnership. We argue that pedagogic partnership may be developed to support the wellbeing of modern-day higher education communities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| 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".