Sustainability: Teaching an Interdisciplinary Threshold Concept through Traditional Lecture and Active Learning
Bibliographic record
Abstract
One of the difficulties in teaching global sustainability in the introductory political science classes is the different emphases placed on this concept and the absence of the consensus on where the overall balance between environmental protection, economic development, and social justice should reside. Like many fuzzy concepts with which students struggle, teaching sustainability lends itself to pedagogical examination within the scholarship of threshold concepts. This article investigates students’ understanding of sustainability in the seven semesters when the concept of sustainability was introduced via role-playing simulation and compares it with the similar data from a more recent semester when simulation was supplemented with traditional lecture and classroom exercises. Ultimately, our research question is twofold: (1) How do students define a multi-faceted concept like global sustainability and (2) what is the better way to teach it – active learning only or active learning in combination with traditional instruction? Certaines des difficultés rencontrées quand on enseigne la durabilité mondiale dans des cours de base de sciences politiques sont les divers accents mis sur ces concepts et l’absence de consensus sur la question de savoir où devrait se situer l’équilibre général entre la protection de l’environnement, le développement économique et la justice sociale. Tout comme c’est le cas avec de nombreux concepts flous qui donnent des difficultés aux étudiants, l’enseignement de la durabilité se prête à un examen pédagogique au sein de la recherche sur les concepts de seuil. Cet article se penche sur la manière dont les étudiants ont compris la durabilité pendant les sept semestres au cours desquels le concept de durabilité a été présenté par le biais de simulation de jeux de rôles et il la compare aux données semblables recueillies lors d’un semestre plus récent au cours duquel la simulation a été supplémentée par des cours magistraux traditionnels et des exercices de classe. En fin de compte, notre question de recherche est double : 1) Comment les étudiants définissent-ils un concept qui présente de nombreuses facettes tel que la durabilité mondiale, et 2) Quelle est la meilleure manière de l’enseigner - exclusivement par un apprentissage actif ou par le biais d’un apprentissage actif combiné à une instruction traditionnelle?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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".