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Record W3107398512 · doi:10.5430/jct.v9n4p75

Impact of Argument-Based Laboratory Method on Scientific Process Skills of Pre-Service Primary School Teachers and Their Views of The Nature of Science

2020· article· en· W3107398512 on OpenAlexvenueno aff
Ceren Köseler, Demet Şahin Kalyon

Bibliographic record

VenueJournal of Curriculum and Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsArgument (complex analysis)Mathematics educationProcess (computing)Control (management)Data collectionScience educationService (business)PsychologyComputer scienceChemistrySociologySocial science

Abstract

fetched live from OpenAlex

The present study aimed to determine the impact of argument-based laboratory method on the scientific process skills of pre-service primary school teachers and their views on the nature of science.The study was designed based on the pretest-posttest quasi-experimental method and conducted with 64 sophomore pre-service primary school teachers (37 in the experimental group, 37 in the control group) studying a Primary Education Department. The dependent variables of the studies are the views of the pre-service primary school teachers on the nature of science and their scientific process skills, while the independent variable of the study was argument-based laboratory application The nature of science scale and scientific process skills tests were used as the data collection tools. The Argument Driven Inquiry approach was employed in the experimental group, while a conventional laboratory approach was implemented in the control group. The findings of the study revealed that the argument-based laboratory method have improved the student views on NOS and their scientific process skills.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.317
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.360
Teacher spread0.346 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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