MétaCan
Menu
Back to cohort
Record W2207256200 · doi:10.5539/jel.v5n1p7

Elementary School Students’ Mental Models about Formation of Seasons: A Cross Sectional Study

2015· article· en· W2207256200 on OpenAlexvenueno aff
Cumhur Türk, Hüseyin Kalkan, Kasım Kıroğlu, N.O. Iskeleli

Bibliographic record

VenueJournal of Education and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyTest (biology)Set (abstract data type)Computer science

Abstract

fetched live from OpenAlex

<p>The purpose of this study is to determine the mental models of elementary school students on seasons and to analyze how these models change in terms of grade levels. The study was conducted with 294 students (5<sup>th</sup>, 6<sup>th</sup>, 7<sup>th</sup> and 8<sup>th</sup> graders) studying in an elementary school of Turkey’s Black Sea Region. Qualitative and quantitative data collection methods were used in the study. The students first were asked 3 open ended questions (one of them was a drawing) in order to determine their mental models on seasons. Following this, the students took an achievement test on seasons that consisted of 4 multiple questions. Quantitative data were analyzed by SPSS 20.0 while the qualitative data were analyzed by the researchers by using content analysis technique. The results of the study showed that the students construct the formation of seasons in various ways in their minds. However, differently from the literature, the presence of some new mental models was found. For a full understanding of the seasons, the necessity of a set of pre-learnings has been recommended. It will be useful to design basic activities based on hands-on and learning by doing which will enable the most effective learning and to put this in the textbooks in the most suitable way. Additionally tangible physical-scale hands-on models, 3D simulation modeling and planetarium environment should be used in students’ education about formation of seasons.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.114
GPT teacher head0.478
Teacher spread0.365 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Education and LearningSame topicScience Education and PedagogyFrench-language works237,207