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Record W4306784090 · doi:10.5430/jct.v11n7p7

Research on the Training Mode of Children's Engineering Thinking with the Concept of STEAM Education

2022· article· en· W4306784090 on OpenAlexvenueno aff
Taibo He, Liang Chen

Bibliographic record

VenueJournal of Curriculum and Teaching · 2022
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationTable (database)Process (computing)Action (physics)Test (biology)Theme (computing)Steam explosionSample (material)Engineering educationPsychologyEngineeringPedagogyComputer scienceEngineering managementChemistry

Abstract

fetched live from OpenAlex

As one of the channels to cultivate compound talents with innovative features, STEAM education, has attracted widespread attention from all walks of life. Engineering thinking, among the core qualities of STEAM education, has gained increasingly growing importance in the K12 stage. The main aim of the study is to analyze the training mode of children's engineering thinking with the concept of steam education. With "paper-cutting" as the theme of the teaching activity, this research selects 16 fourth-grade primary school students as the research objects and carries out three rounds of teaching activities under the framework of STEAM activities for the cultivation of engineering thinking capability. Through three rounds of iterations with the action research method, as well as the overall scoring table and the sub-item scoring table to conduct paired sample t-test on the data of the three rounds of the teaching process, it is found that STEAM education has a significant effect on improving children's engineering thinking capability.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.266
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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