Triggering emotions as a tool to increase involvement of students and foster learning and development
Bibliographic record
Abstract
Introduction. We are often faced with students who do not apply what they learn at university in their practice. Our team worked on a teaching design in order to use emotions as cornerstone of students’ involvement in their studies and, most importantly, in the investment of the acquired knowledge in their praxis. Although they play fundamental role in human development, emotions are seldom used as a starting point in education. Material and Methods. The research led us to conceive a training method based on pictures to provoke emotions. In turn, these emotions trigger a response from students, leading them to be eager to learn, persevere and try to implement new knowledge in their field of activity. We present parts of our theoretical framework that underpin our training method. We describe some challenges that we faced during its implementation. Finally, we give an example of using this method in a classroom of agricultural trainers. We applied our method to the training of farmers about the ways to counter the effects of climate change. Results. The trainers reacted strongly to the pictures and showed high involvement in the workshop. After applying these methods in teaching farmers, they reported a higher motivation and willingness of farmers to implement new knowledge. Conclusion. The emotions triggered by pictures seem to be able to form the cornerstone of an effective approach to teaching and learning. This method holds promise for the practical application of knowledge gained in the classroom. It may also drive long-lasting changes in the students’ approach to teaching. This calls for more research on the possible use of emotions and art to foster learning at different levels of schooling.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".