Methodological Proposal for Graphic Design Education Aimed at Fostering Social and Environmental Responsibility
Bibliographic record
Abstract
Abstract According to the United Nations and its Sustainable Development Goals (SDGs), educational systems must adapt their learning objectives and methods to develop skills that promote sustainable development (Rieckmann et al., 2017). This chapter explores an ongoing doctoral research that utilizes a methodology enabling education to shape the role of graphic designers within the socio-ecological transition in Quebec. In class, graphic design students typically follow “creative briefs” that simplify problem complexity (Haylock, 2020), which limits their autonomy and sometimes prevents them from addressing social and environmental concerns. When they undertake projects independently, they often prioritize personal expression over the needs of users and stakeholders (Valencia et al., 2021). To address this, the research employs Learning Experience Design (LXD), an emerging transdisciplinary discipline focused on creating effective and engaging learning experiences. The primary objective is to implement LXD to tailor learning for graphic design students at Cégep de Sainte-Foy. Specifically, it adopts responsible entrepreneurship as a pedagogical approach, empowering students to initiate their own projects while fostering social and environmental responsibility. Employing a Mixed Methods Research (MMR) approach, the study combines quantitative and qualitative data collection through questionnaires, interviews, co-design workshops, and checklists. Notably, the research stands out for its collaborative nature, involving stakeholders such as students, faculty, administrative bodies of Cégep de Sainte-Foy, and external experts. This chapter presents initial results demonstrating the potential of education to prepare learners for meaningful contributions to sustainable development, emphasizing the transformative role of tailored pedagogical approaches like LXD in fostering responsible design practices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".