Continuing teacher education with a view to education for sustainable development: challenges of integration between mathematics and science
Bibliographic record
Abstract
Environmental education has been discussed worldwide throughout society, therefore an increase in actions that promote environmental awareness is essential and the need for the formation of critical, collective and sustainable thinking is emerging. Based on municipal, state and federal laws, the National Curricular Parameters and the National Common Curricular Base report that good academic performance would be of no use apart from the perception of the impact of individual and collective human actions on the environment. However, society has been facing challenges in the continued training of teachers, and developing tools that support the work of these professionals, including those in the Science area, through interdisciplinarity in the initial years, becomes essential. This article reinforces the need to provide such programs, which aim to develop methodological practices that seek lesson plans focused on environmental themes. The research was based on the challenges that teachers face when they propose to develop environments in their classrooms that are favorable to promoting social contexts. In addition to investigating what characteristics are necessary for the development of activities that support collaborative work, allowing the teacher to develop a role as a mediator and not just a transmitter of knowledge.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".