Tecnologias Digitais no Ensino de Biologia: Ferramentas e Impactos no Aprendizado
Bibliographic record
Abstract
Introdução: Este artigo aborda o uso de tecnologias digitais no ensino de Biologia, destacando sua crescente relevância no contexto educacional moderno. Objetivo: O objetivo deste estudo é investigar os impactos dessas tecnologias no aprendizado, avaliando sua eficácia e os desafios associados à sua implementação. Método: Foi realizada uma revisão integrativa da literatura, analisando estudos recentes publicados entre 2018 e 2024. Resultados: Os resultados indicam que as tecnologias digitais, como jogos educativos, modelos 3D, podcasts e plataformas online, quando utilizadas de forma estratégica e equilibrada, podem enriquecer o ensino de Biologia, promovendo maior engajamento e retenção de conhecimento. No entanto, desafios como a desigualdade no acesso à tecnologia e a necessidade de capacitação contínua dos professores foram identificados. Conclusão: A conclusão destaca a importância de integrar essas tecnologias de maneira cuidadosa, garantindo que todos os alunos possam se beneficiar plenamente dessas inovações.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".