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Record W4400958009 · doi:10.5539/ies.v17n4p111

Proportions of Cartoons in Elementary School Instruction: Teacher Perspectives

2024· article· en· W4400958009 on OpenAlexvenueno aff
Wanicha Sakorn, Siriwiwat Lata

Bibliographic record

VenueInternational Education Studies · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyPrimary educationPedagogyTeaching method

Abstract

fetched live from OpenAlex

This study investigates elementary school teachers’ perceptions of the appropriate proportions of cartoons for instructional purposes, with a focus on the Thai educational context. The research aims to shed light on teachers’ preferences for cartoon proportions across different grade levels, contributing valuable insights into the effective use of cartoons in elementary school instruction. A questionnaire-based approach was employed to gather data from 78 elementary school teachers. The study found that teachers held a positive perception of cartoons as effective tools for teaching primary school students, with participants favoring distinct cartoon proportions for different grade levels. Notably, larger cartoon scales were preferred for early grades (Grade 1 and Grade 2), while smaller scales found favor with older students in Grades 4, 5, and 6. These findings highlight the nuanced considerations educators make when integrating cartoons into their instructional materials and underscore the potential of cartoons to enhance the quality of elementary school education.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.506
Teacher spread0.407 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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