The Impact of Project‐Based Learning on Student Knowledge Exchange for Sustainability: The Case for University–Business Collaborations
Bibliographic record
Abstract
ABSTRACT Knowledge exchange in higher education is an emerging area delivered in multiple ways, including university–business collaboration, combining academic knowledge and business needs. Knowledge exchange can act as a vehicle for embedding sustainability in the curriculum and help address significant challenges we face as a society. Student knowledge exchange is driven by students who work on real‐world projects, often with businesses involved. There is a need to assess the impact of knowledge exchange on students to inform curriculum design and development for a better student experience and outcomes. This research aimed to better understand the impact of university–business collaboration on student knowledge exchange for sustainability by adopting project‐based learning pedagogy. The study draws lessons from the School of Architecture, Design and the Built Environment and Nottingham Business School at Nottingham Trent University. The study found that project‐based learning significantly impacts students' sustainability knowledge and competencies. Besides knowledge and competencies, students who work with businesses also gain sustainability skills, attitudes, and behaviours. The design and implementation of project‐based learning affect the outcomes, including activities integrated into the curriculum versus extracurricular activities, bespoke versus ad hoc student projects and the duration of students' exposure to sustainability‐related topics. This study contributes to higher education teaching and learning and impacts students' capacity building, affective domain and career readiness. Project‐based learning can enhance student knowledge exchange for sustainability, particularly when collaborating with businesses, impacting students and businesses.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".