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Record W4311017874 · doi:10.5753/sbie.2022.225250

Lições aprendidas usando Robótica Desplugada, Linguagens Baseadas em Blocos e Simulador Robótico 3D

2022· article· pt· W4311017874 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnais do XXXIII Simpósio Brasileiro de Informática na Educação (SBIE 2022) · 2022
Typearticle
Languagept
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsnot available
FundersCanadian Bureau for International Education
KeywordsHumanitiesComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

O avanço da tecnologia mudou a rotina do ser humano em diversos aspectos, e com isso, a demanda por profissionais de computação aumenta. Todavia, o exercício do pensamento computacional no ensino básico é escasso, principalmente em sociedades mais pobres. Desta forma, este artigo propõe um método para exercitar o pensamento computacional utilizando conceitos de matemática básica de maneira conjunta com duas estratégias bem sucedidas: a Robótica educacional e a utilização de linguagem visual baseada em blocos gráficos arrastáveis. O UpRobotics foi criado para o público escolar infantil, capaz de explorar conhecimentos científicos dos alunos, capacitando-os para manipular um braço robótico, semáforo virtual e carrinho em simulador 3D.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0060.001
Scholarly communication0.0040.002
Open science0.0060.005
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.281
Teacher spread0.263 · 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