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Record W2807153132 · doi:10.5430/ijhe.v7n3p107

An Investigation of Primary Student Teachers’ Drawings of the Human Internal Organs

2018· article· en· W2807153132 on OpenAlexvenueno aff
Yılmaz Çakici

Bibliographic record

VenueInternational Journal of Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsStomachPancreasClass (philosophy)Human anatomySpleenMedicineMathematics educationMedical educationPsychologyInternal medicineAnatomyComputer science

Abstract

fetched live from OpenAlex

The aim of this study is to investigate primary student teachers’ drawings of the human internal organs, e.g. location, size and presence of organs (heart, lungs, stomach, liver, kidneys, pancreas and intestines etc.) This research was conducted with 104 primary teacher candidates studying in the Faculty of Education at Trakya University during the 2016-2017 academic year. The research data was obtained through the use of drawings and interviews with selected students. The findings revealed that, as expected, all students showed the heart, lungs and stomach in their drawings. The most frequently illustrated other human internal organs were intestines, liver, kidneys, pancreas and spleen respectively. However, too many of them were not scientifically acceptable in terms of the location and size. There are many points in the students’ drawings that need to be underlined by teachers during the teaching-learning process in class.

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)0.009
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.437
Teacher spread0.392 · 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
Published2018
Admission routes1
Has abstractyes

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