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OS DESAFIOS DA FORMAÇÃO DE PROFESSORES PARA UTILIZAÇÃODAS TIC NO ENSINO FUNDAMENTAL II

2018· article· pt· W2904094618 on OpenAlexaff
Jonas Rafael Nikolay, Ademir Aparecido Pinhelli Mendes

Bibliographic record

VenueApresentações Trabalhos Científicos · 2018
Typearticle
Languagept
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Setor Educacional: EDUCAÇÃO INFANTIL E FUNDAMENTAL RESUMOEste relato de experiência problematiza o desafio da formação de professores para utilização das TIC na mediação do ensino e da aprendizagem no século XXI.Parte da hipótese que, mesmo havendo um grande caminho a ser percorrido, é possível observar avanços significativos acontecendo na escola.Tem por objetivo identificar os fatores que distanciam e aproximam os professores do uso das TIC na prática docente.A fundamentação teórica está baseada em Kenski (2012), Moran (2013), Christensen, Horn e Johnson (2012) e Mattar (2017).A pesquisa é qualitativa e analisa os dados obtidos por meio de um questionário online respondido por professores que atuam na Educação Básica e participaram de uma oficina para o uso de TIC em sala de aula.Foram encontradas evidências que mostram resultados significativos ocorrendo nas escolas a partir da formação de professores para utilização das TIC

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)0.033
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.095
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0080.003
Scholarly communication0.0110.004
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.119
GPT teacher head0.390
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Published2018
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