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Record W2124114729 · doi:10.5539/ass.v10n8p90

Direct and Indirect Impact of Perceived School Climate upon Student Outcomes

2014· article· en· W2124114729 on OpenAlexvenueno aff
Gulnaz Zahid

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsSocioemotional selectivity theoryPath analysis (statistics)PsychologyPerceptionSchool climateStudent engagementDevelopmental psychologyStructural equation modelingMathematics educationStatistics

Abstract

fetched live from OpenAlex

This research aims at investigating and comparing the direct and indirect impact of children’s perceptions of school climate upon their academic performance and socioemotional adjustment. A model was developed in which children’s perception of school climate was considered as the independent variable and student academic performance and socioemotional adjustment as the dependent variables. Within this model, three mediating variables were selected which were children’s perceptions of parental involvement, academic motivation and student academic engagement. The mediators indicate three broad categories, i.e., school, home and student-specific variables, which facilitate comparing the significance of their role in the model. Data was collected from 324 students from Grades 7 and 9 and only the complete data from 268 cases (girls=126, boys=142) was analyzed. Two independent models were tested through path analysis. Findings revealed differential roles of the selected mediators for the student outcomes. This study presents a significantly useful model to understand the impact of school climate and provides baseline information for the implementation of the National Education Policy (2009), which focuses upon the improvement of learning environment of the schools. On the basis of findings, conclusion and recommendations have been presented.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.033
GPT teacher head0.403
Teacher spread0.370 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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