APRIMORANDO O ENSINO DAS CIÊNCIAS: METODOLOGIAS ATIVAS NO PROGRAMA DE RESIDÊNCIA PEDAGÓGICA
Bibliographic record
Abstract
Introdução:No vasto campo das ciências e tecnologias, a diversidade de recursos disponíveis, guiados por metodologias específicas, tem o potencial de exercer uma influência positiva no processo de ensino-aprendizagem.Entre essas abordagens, as metodologias ativas se destacam pela sua capacidade de integrar os elementos fundamentais da educação, cultura, sociedade, política e escola.Implementadas por meio de estratégias dinâmicas e inovadoras, essas metodologias são centralizadas na participação ativa do aluno, buscando otimizar de maneira eficaz o caminho educacional Objetivo: Este trabalho tem como propósito reafirmar as vantagens do uso das metodologias ativas, fundamentando-se nas experiências vivenciadas no Programa de Residência Pedagógica (PRP).Relato de caso/experiência: No contexto do PRP, a regência em escolas de educação básica proporciona uma oportunidade única para testar teorias educacionais aprendidas em sala de aula.Essa experiência se configura como um laboratório experimental de estratégias e propostas inseridas no processo educacional, evidenciando a aceitação
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".