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Record W4406586584 · doi:10.5539/ies.v18n1p13

Autonomy-Supportive Classroom Climate and Attitudes Towards Social Participation: A Practice in Mixed-Grade Classes in a Japanese Elementary School

2025· article· en· W4406586584 on OpenAlexvenueno aff
Ryo Okada

Bibliographic record

VenueInternational Education Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyPsychologyMultimethodologyMathematics educationSchool climatePedagogyPolitical science

Abstract

fetched live from OpenAlex

This study focuses on the relationship between an autonomy-supportive classroom climate and changes in attitudes towards social participation in mixed-grade classes. The research field involved the learning activities conducted in a Japanese elementary school. Learning activities were carried out in mixed-grade classes, and the children engaged in problem solving with their peers from different grades in collaboration with the local community. It was hypothesized that a perceived autonomy-supportive classroom climate would affect attitudes towards social participation and that this effect would be mediated by intrinsic motivation and reflection. Participants were 163 third- and fourth-grade children from mixed-grade classes. Data were collected repeatedly over a three-year period through a questionnaire survey. Latent growth curve modeling analyses revealed that an autonomy-supportive classroom climate was related to intrinsic motivation, which in turn, was related to reflection. Additionally, intrinsic motivation and reflection predicted the initial level of attitudes towards social participation. Intrinsic motivation was negatively related to the extent of change in attitudes towards social participation. These results suggest that an autonomy-supportive classroom climate promotes children’s positive attitudes towards social participation. In mixed-grade classes, teachers must create classrooms in which children can support each other’s autonomy.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.088
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.052
GPT teacher head0.484
Teacher spread0.432 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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