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Record W3015342089 · doi:10.1139/cjp-2018-0444

Development and application of a concept test on the subject of stars

2020· article· en· W3015342089 on OpenAlexvenueno aff
Ebru Ezberci-Cevik, Mehmet Altan Kurnaz

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsOptimal distinctiveness theoryTest (biology)Cronbach's alphaConformitySubject (documents)Reliability (semiconductor)Mathematics educationSubject matterPhysicsPsychologyStatisticsSocial psychologyComputer sciencePedagogyCurriculumPsychometricsMathematics

Abstract

fetched live from OpenAlex

The aim of this study is to develop a concept test on the subject of stars that is suitable for model analysis and to evaluate the conformity of this model. The study was performed using a screening model, which is a type of quantitative research method. The study also tried to fill the gap in the existing literature regarding the use of quantitative methods. To develop the test, existing studies on stars were first examined; interviews with candidates who were teachers were conducted; and finally, necessary amendments to test items were made after referring to experts’ opinions. To assure its validity and reliability, the test was applied to a total of 175 candidates who were studying at schools of education in the departments of science teaching of three different universities to become teachers; all candidates took astronomy courses and were educated on the subject matter. The final form of the test was comprised of 26 multiple-choice questions, each with 5 possible answers. The Cronbach’s alpha reliability coefficient of the test was calculated to be 0.735. In addition, the mean strength of the test was found to be 0.370 and the distinctiveness was found to be 0.390. Statistical analyses revealed that the concept test developed in this study is a valid and reliable test that conforms to the model analysis.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.076
GPT teacher head0.320
Teacher spread0.244 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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