O uso de mídias no Programa Mais Educação: contribuições para o processo de aprendizagem
Bibliographic record
Abstract
Esse artigo apresenta os resultados obtidos a partir do projeto de intervencao realizado em duas turmas do Programa Mais Educacao no municipio de Almenara – MG. A proposta analisou a contribuicao das midias computador, jornal e TV para o processo de ensino e aprendizagem. Perpassou-se pela pesquisa teorica com textos de autores que discorrem sobre o tema proposto “O uso de midias no processo de aprendizagem”, entre eles, Valente (2005), Moran (2011), Perrenoud (2000). A intervencao fora desenvolvida com 30 alunos do 5o ano do Ensino Fundamental e teve como metodologia, alem da pesquisa teorica, a tecnica da observacao, entrevista verbais, utilizadas como diagnostico, para aplicacao da intervencao. Constatou-se que apesar de a escola possuir instrumentos que podem subsidiar um trabalho dinâmico com os alunos os mesmos nao sao utilizados exatamente pela falta de preparo dos professores. Isso hoje e antagonico ao exposto pela midia e pela tecnologia, que mostram nossos alunos integrados atraves das redes sociais. Verificou-se a necessidade de formacao dos professores para o uso integrado das midias no contexto escolar e o desejo dos alunos de estarem integrado neste mundo de novas linguagens.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".