Design thinking for supporting citizens in climate change adaptation
Notice bibliographique
Résumé
Design thinking, which includes consideration of user needs, abduction and rapid prototyping, is a promising collaborative approach in education for sustainability. Our research team has experimented design thinking with citizens on various environmental issues, including climate change adaptation. Our results demonstrate that design thinking promotes a broader understanding of the issues by the participants, motivates them, strengthens their empathy towards users, identifies their real needs, leads to many solutions, while mobilizing certain high-level skills. However, design thinking requires time and multidisciplinary work. It is limited by the knowledge of the solvers, by the short-term consideration of the problems and by the emphasis placed on the human being. What design thinking process would be optimal to support citizens in adapting to climate change? Climate change is a complex environmental problem. In this area, resolvers who wish to adapt must deal with poorly defined initial situations and unpredictable climatic risks. For example, if little is known about the state of health of a watercourse and the lifestyles of local residents, it will be difficult to propose adaptations that will make the watercourse water and citizens resilient to floods or droughts. An understanding of the initial social, ecological and economic situations is therefore necessary to properly target the adaptations that will be applied locally. During a process of adaptation to climate change, problem solvers must therefore be invited to properly represent the social and scientific issues of climate change and to mobilize their systemic and forward thinking. Knowing each their share of local situations, resolvers must share their perspectives to collectively compose a credible portrait of situations, sub-situations and likely subsequent situations. The formulation of solutions must also mobilize certain skills in the solvers, including creativity, critical thinking and strategic planning. These desired qualities for the solutions require a support process that invites the solvers to mobilize their capacities for innovation, critical judgment and planning. In the ClimAction program, we designed a design thinking process, conducive to education on adaptation to climate change. High school students are challenged to improve a local waterway to make it more resilient to floods or droughts. The program uses design thinking as well as the mobilization of adaptation skills: systemic, predictive, creative and critical thinking; communication; strategic planning. Students study the health of a local stream using scientific indicators: presence of macro-invertebrates and health of fish. They interview local citizens to find out their needs and uses of the watercourse. They then represent the initial situation of the river in its social and scientific aspects. They predict the possible impacts of floods and droughts on the watercourse. Ideation and rapid prototyping allow them to propose, choose and implement an adaptation action that they communicate to the population. The ClimAction program uses techniques of visual representation, helping students to structure an environmental issue and to predict the possible futures of a watercourse. We also want to develop their feelings of being able to act. The presentation sums up our research on design thinking and discusses the ClimAction program.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,008 |
| Communication savante | 0,010 | 0,010 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».