Teaching life cycle assessment in higher education
Bibliographic record
Abstract
Purpose: Scientific Life Cycle Assessment (LCA) literature provides some examples of LCA teaching in higher education, but not a structured overview of LCA teaching contents and related competencies. Hence this paper aims at assessing and highlighting trends in LCA learning outcomes, teaching approaches and developed content used to equip graduates for their future professional practices in sustainability. Methods: Based on a literature review on teaching LCA in higher education and a collaborative consensus building approach through expert group panel discussions, an overview of LCA learning and competency levels with related teaching contents and corresponding workload is developed. The levels are built on the European Credit Transfer and Accumulation System (ECTS) and Bloom's taxonomy of learning. Results and discussion: The paper frames five LCA learning and competency levels that differ in terms of study program integration, workload, cognitive domain categories, learning outcomes, and envisioned professional skills. It furthermore provides insights into teaching approaches and content, including software use, related to these levels. Conclusions and recommendations: This paper encourages and supports higher educational bodies to implement a minimum of 'life cycle literacy' into students' curriculum across various domains by increasing the availability, visibility and quality of their teaching on life cycle thinking and LCA.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".