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Record W2238462640 · doi:10.5753/cbie.wcbie.2015.1464

Desafios e oportunidades ao ensino do pensamento computacional na educação básica no Brasil

2015· article· pt· W2238462640 on OpenAlexfundno aff
Rozelma Soares de França, Patrícia Tedesco

Bibliographic record

VenueAnais ... Workshops do Congresso Brasileiro de Informática na Educação · 2015
Typearticle
Languagept
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsnot available
FundersCanadian Bureau for International Education
KeywordsPhysicsHumanitiesPolitical scienceMathematics educationPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Atualmente é requerido dos estudantes desenvolver diversas habilidades, dentre elas o pensamento computacional. Contudo, no Brasil o ensino de tal habilidade não integra o currículo escolar. Nesse sentido, este artigo discute desafios ao ensino do pensamento computacional na educação básica brasileira, apresentando oportunidades de pesquisa na área. Além disso, é apresentada uma proposta para minimizar alguns dos problemas apontados, a qual demonstrou contribuir com a aprendizagem de estudantes do nível médio durante um curso de desenvolvimento de jogos digitais.

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)0.005
metaresearch head score (Gemma)0.017
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0040.004
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.087
GPT teacher head0.357
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
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

Citations26
Published2015
Admission routes1
Has abstractyes

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