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Record W3011093141 · doi:10.4322/gepem.2019.003

Tarefas de geometria dinâmica com objetos de aprendizagem para a exploração e a investigação de conceitos geométricos

2019· article· pt· W3011093141 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

VenueBoletim GEPEM · 2019
Typearticle
Languagept
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des Laurentides
Fundersnot available
KeywordsAnimationComputer scienceMathematics educationMathematicsGeometryHumanitiesComputer graphics (images)Philosophy

Abstract

fetched live from OpenAlex

Tarefas  de  geometria  dinâmica  têm  um  papel  fundamental  no  ensino  e  aprendizagem  de matemática, pois podem ampliar as possibilidades de abordagem dos conceitos geométricos estudados na Educação Básica. Assim, o objetivo deste artigo foi o de apresentar e caracterizar tarefas de geometria dinâmica que envolvem o uso de Objetos de Aprendizagem (OA) para a exploração e a investigação de conceitos geométricos. As tarefas foram elaboradas considerando os princípios metodológicos teorizados por Powell e Alqahtani (2015) e Powell e Pazuch (2016) para um trabalho investigativo usando Softwares de Geometria Dinâmica (SGD). A primeira tarefa foi desenvolvida  para  abordar  as propriedades geométricas dos quadriláteros,  enquanto  a  segunda permite o trabalho com as transformações geométricas presentes na animação do OA, o qual foi elaborado com o software GeoGebra. Os resultados mostram que essas tarefas podem contribuir para a prática do professor que ensina geometria e deseja integrar tecnologias digitais a seu trabalho em sala de aula.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.377
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.085
GPT teacher head0.346
Teacher spread0.261 · 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