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Record W3033903751 · doi:10.5430/jct.v9n2p95

The Impact of Nature Education on Turkish Students’ Affective Tendencies towards the Environment and Scientific Curiosity

2020· article· en· W3033903751 on OpenAlexvenueno aff
Gülbin Özkan, Ünsal Umdu Topsakal

Bibliographic record

VenueJournal of Curriculum and Teaching · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychological and Educational Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCuriosityTurkishAffect (linguistics)PsychologyTRIPS architectureMathematics educationScale (ratio)Test (biology)PedagogySocial psychologyEngineeringGeographyEcologyCartographyCommunication

Abstract

fetched live from OpenAlex

The aim of this study is to investigate the impacts of nature education on the affective tendencies and science curiosities of students. In this study, one group pretest-posttest test design was used. The study group consists of 30 seventh grade students studying in Istanbul, Turkey. In the study, nature education implementations were carried out as 14 activities for five days. Some activities were held in university laboratories, some in trips, and out-of-school settings. The data of the study were collected by the Environment Affect Scale and Children’s Science Curiosity Scale. The quantitative data collected were analyzed with the SPSS program. As a result of this study, it was found that the students' affective tendencies towards the environment improved significantly after the implementation. In addition, it was observed that students' scientific curiosity increased positively.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.032
GPT teacher head0.399
Teacher spread0.368 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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