Expansive Learning of Preservice Teachers Teaching Sustainable Development during Their Practicum
Bibliographic record
Abstract
Education for Sustainable Development (ESD) is a complex and multifaceted subject, including many aspects, environmental, societal, and financial. It is not a set of knowledge, which can be learned, because it is an evolving subject and in addition solutions that are successfully applied at specific locations might fail elsewhere. ESD should make students aware of the problems humankind is facing and encourage them to become active citizens. Thus, people must also have positive attitudes towards sustainability issues. In this study, we will present the results of a teaching intervention (TI) leading to a system of two expansive learning cycles. For the TI we used the topic of houses, which are a social construct, an economic entity, and have environmental influence. The researcher, an architect had to transform her knowledge to prepare the TI, thus starting an expansive learning cycle which was influenced by the outcome of the TI. The preservice teachers, who decided to use the topic of houses during their internship started their expansive cycle, which again influenced the researchers’ learning cycle. In this study, we will present the results of the TI and the implementation of the preservice teachers’ teachings of SD during their practicum. The preservice teachers reflected on their teachings in their written reports, which were used to analyze how preservice teachers chose to apply the topic ‘houses’ during their internship.
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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".