Lights, Camera, Reaction: Evaluating Extent of Transformative Learning and Emotional Engagement Through Viewer-Responses to Environmental Films
Bibliographic record
Abstract
In sustainability education affective responses to climate change are rarely discussed, and this is to the detriment of students. One way to address this gap in higher education for sustainability is learner-centred teaching using transformative learning principles. The processes for implementation may vary. Our preliminary study evaluated the contribution of environmental films paired with viewer-response activities, such as reflections and discussions, to create emotional engagement and facilitate transformative learning in an online course where content focused on sustainability and climate change. Data for the study were gathered through two questionnaires, student reflections, and interviews. Our study found that the process of film watching, reflection writing, and engaging in discussion was conducive to incorporating five of the six elements of transformative learning: individual experience, promoting critical reflection, awareness of context, dialogue, and authentic relationships. We conclude that films are an effective means of conveying complex content in an online course pertaining to climate change and sustainability. We propose pairing films with viewer-response strategies, especially reflections to allow students to identify their feelings, biases, and preconceived frames of references and stimulate the path toward transformative learning in higher education.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".