Promoting the wellbeing of students and the sustainability of a university campus: solutions from a design thinking experiment
Bibliographic record
Abstract
Purpose The purpose of this paper is to conduct a design thinking (DT) experiment with graduated students to generate solutions for improving student wellbeing and facilitating the ecological transition of a university campus. Design/methodology/approach Over a two-month period, seven graduates from a teaching program (the designers) participated in a DT experiment with a research team to find solutions to improve a university campus in Eastern Canada. The conceptual challenge concerned student wellbeing and the ecological transition of the outdoor campus. A content analysis was performed on the solutions generated by the designers using two frameworks: Dodge’s model of wellbeing and the Sustainable Development Goals (SDGs) framework for the sustainability of the institution. Findings The authors explore the solutions proposed by the designers, which were mainly related to wellbeing and quality of education (SDGs 3–4), but also to the terrestrial environment (SDG 15) and to inclusion and equity (SDGs 10–16). The solutions for the physical wellbeing of the students emphasized designing a campus with sport facilities. Psychological solutions focused on creating spaces for relaxation and fostering a sense of belonging. The solutions for social wellbeing involved building spaces for socializing and studying. The results generate solutions according to an emerging fourth dimension to wellbeing: the environmental dimension. These solutions involved greening the campus to encourage a connection between students and nature while improving the quality of education and inclusion by means of decolonizing and Indigenizing education. Another finding acknowledged the relevance of DT as a significant tool for the ecological transition of universities. Research limitations/implications This study focused on one university campus and a single DT experiment. The designers’ solutions were not tested on the campus but were submitted to a group of international students for feedback. Even if the solutions are supported by some authors in the field of higher education, there is a need to test the solutions proposed in this experiment to better understand their potential impacts. More research on DT in universities is needed to find innovative solutions and assess their impact on student wellbeing and sustainability in Canadian higher education. Practical implications The solutions presented in this study can inspire universities and communities in their actions toward a more caring society, and in their ecological transition to reach as many SDGs as possible for a sustainable future. By adopting collaborative processes such as DT, university and city stakeholders may find ways to engage in sustainable paths. Originality/value To the best of the authors’ knowledge, this study is the first of its kind in Eastern Canada to analyze solutions generated by a DT experiment in the context of developing a university campus that fosters student wellbeing and sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".