The joy of teaching and learning in academia – teachers’ perspectives from three countries
Bibliographic record
Abstract
All over the world, the educational landscape has changed dramatically over the last year, impacting the way we teach and learn. It is time for reflecting and searching for new ways to support each other, during these pandemic times and beyond; time to co-construct creative partnerships and to innovate new ways to co-create. Change is an inevitable part of teaching and learning but the adaptations currently required are of unprecedented scale. How can we teach and learn with joy in today’s academia? How can we support each other, as teachers, in more creative ways? These two reflective questions were at the base of the study, which was conducted by university teachers from three countries: “Lucian Blaga” University of Sibiu, Romania; London Metropolitan University, United Kingdom; and University of Calgary, Canada. The methods used included interviews, focus groups and free writing with colleagues in each university. Findings revealed the challenges faced by each participant due to the emotional pressure caused in these supercomplex times, and the struggle to bring joy of teaching and learning in creative ways. This small ethnographic project reveals a need to shift our thinking about emotions and how we may facilitate the greatest success of all our students, by continually inventing new solutions and teaching with enthusiasm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".