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METODOLOGIAS ATIVAS NA EAD: ACELERAÇÃO DIGITAL NO PROCESSO DE ENSINO E APRENDIZAGEM NA ESCOLA DE NEGÓCIOS DAS ARTESÃS

2021· article· pt· W3160725646 on OpenAlexaff
Angélica Florentino de Oliveira, Fernanda Goulart da Silva, Yasmin Rizzo Soares, Fernanda Galvão Sklovsky

Bibliographic record

VenueRevista Brasileira de Aprendizagem Aberta e a Distância · 2021
Typearticle
Languagept
FieldSocial Sciences
TopicEducation during COVID-19 pandemic
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

O ensino a distância teve um crescimento exponencial nos últimos meses, impulsionado pela crise da pandemia da Covid-19, que exigiu medidas de isolamento social. Por conseguinte, o presente artigo temcomo propósito apresentar o processo de utilização de metodologias ativas para acelerar o ciclo de ensino e aprendizagem na Escola de Negócios das Artesãs da Rede Asta, em seus pilares: Educação, Design e Mercado. As considerações finais corroboram que as mudanças realizadas na escola foram vitais para que as artesãs desenvolvessem novas habilidades, acessassem novos ambientes de aprendizagem, continuassem ativamente com os seus negócios e pudessem se reinventar frente aos desafios domercado atual.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.005
Scholarly communication0.0100.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.050
GPT teacher head0.359
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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