Teaching Industrial Ecology to Undergraduate Students: Lessons Learned
Bibliographic record
Abstract
Industrial ecology (IE) education is a topic that has received attention in institutions of higher education throughout the world. Some universities have been teaching and researching IE for the past 20 years but its scope is still being defined. There is a need to catalog and exchange experiences of IE teaching and their results. Based on a literature review, this paper aims to provide a framework composed of content and strategies for IE teaching. This framework is tested in teaching IE as a general education course for undergraduate students at Beijing Institute of Technology (BIT). The methodology applied in this paper is of a descriptive and empirical nature and thus this paper focuses on a practice-oriented perspective in describing the experience of BIT. A range of different strategies, including lectures, group discussions, case studies, role playing games, oral presentations, evaluation, and student feedback have been incorporated in the course. The results show that the course received a highly positive evaluation by the participating students. Students were impressed by IE’s characteristics of interdisciplinarity, ways of thinking, and practical value. Additionally, the course appears to influence students’ values, attitudes, and behavioral intentions. The paper concludes with some thoughts on ways of improving IE education in the future.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".