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Record W4292423771 · doi:10.5430/wje.v12n4p1

Pre-Service Teachers' Levels of Understanding the Light-Related Concepts

2022· article· en· W4292423771 on OpenAlexvenueno aff
Ali Yıldız

Bibliographic record

VenueWorld Journal of Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyData collectionKnowledge levelPedagogyMathematics

Abstract

fetched live from OpenAlex

The aim of the study is to investigate the level of understanding of light-related concepts of teacher candidates who took the “Basic Science in Primary School” course in the classroom teaching undergraduate program. This research is a descriptive study. The study group consists of 65 teacher candidates, 51 female, and 14 male studying in the first year of the classroom teaching undergraduate program at a state university. In the study, an opinion form containing five open-ended questions prepared by the researcher was used as the data collection tool. The grouped answers of the teacher candidates, and their background knowledge were calculated and separately transferred to the relevant tables for each question. For each table, the inferences, and comments about the grouped expressions of the participant teacher candidates have been provided. In addition, interviews were conducted with six randomly selected participants. It has been revealed that the pre-service teachers' scientific knowledge about the concepts related to the light is not at the desired level. Based on the findings of the study, some suggestions were made.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.106
GPT teacher head0.335
Teacher spread0.230 · 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 designObservational
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

Citations5
Published2022
Admission routes1
Has abstractyes

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