Teaching the “heads, hearts, and hands” of futures literacy in sustainability education using radical seeds of change
Bibliographic record
Abstract
There is a need for sustainability education to offer students a concrete means to imagine and enact more sustainable futures. Students need to develop futures literacy that is both prefigurative, imaginative, and creative as well as critical and able to challenge existing power. Furthermore, futures education needs to engage students’ “heads, hearts, and hands”: their knowledge, affective orientations, and skills. This paper investigates the use of the “Seeds of Good Anthropocenes” (SoGA) approach in an educational context. “Seeds” are radical initiatives, projects, and practices that currently exist, but that are not yet mainstream. Our research was developed by teachers, teaching assistants, students, and focus country experts involved in a second year mandatory BSc course at Utrecht University. In this course, student teams work with focus country experts to find and combine seeds into transformation pathways to aspirational futures for different national contexts worldwide. Students then use the X-Curve to develop their pathways and explorative future scenarios to test the key assumptions made for these pathways. In this paper, we investigate how the SoGA approach impacts students’ learning and affective orientation about transformative futures. To do this, an extensive qualitative survey was conducted with 92 students, supported by feedback meetings and conversations with all student teams to reflect on their learning experience. Students developed an expanded sense of what futures are possible and of the challenges of systems change (heads). The course process made many students more hopeful, but also more concerned about the future (hearts). Finally, students learned new methods for engaging with the future but also struggled to work internationally (hands). We conclude that using seeds can be powerful for the development of futures literacy in educational contexts, but that their bottom-up character also has limitations that require complementation by other methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".