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Record W2971485288 · doi:10.22456/1679-1916.95703

Aprendizagem Baseada em Projetos na Informática em Saúde: Desenvolvendo Aplicativos com App Inventor

2019· article· pt· W2971485288 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRENOTE · 2019
Typearticle
Languagept
FieldSocial Sciences
TopicScience and Education Research
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

informática se tornou essencial na área da saúde. Políticas de informatizaçãovem sendo implantadas pelo governo brasileiro para capacitar profissionais e acadêmicos, estabelecendo novas relações com a tecnologia e permitindo idealizar novas aplicações de recursos computacionais. O objetivo desse trabalho é relatar e avaliar uma experiência de ensino baseada na Aprendizagem Baseada em Projetos em uma disciplina de Informática Aplicada à Saúde para graduação. Na disciplina, foi proposto o desenvolvimento de aplicativos móveis para a saúde. A turma contou com a participação de 21 alunos que foram avaliados qualitativamente por dois surveys. Todos os alunos completaram a disciplina e relataram um impacto positivo na sua formação. Cinco aplicativos foram construídos e uma nova percepção de tecnologia foi adquirida, 15 estudantes afirmaram estar motivados para aprender novas tecnologias e 16 se consideraram aptos para desenvolver novos aplicativos.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.012

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 head0.069
GPT teacher head0.376
Teacher spread0.307 · 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