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Record W4383341278 · doi:10.3389/feduc.2023.990204

How do parents and school staff conceptualize parental engagement? A primary school case study

2023· article· en· W4383341278 on OpenAlexaboutno aff
Cat Jones, Olympia Palikara

Bibliographic record

VenueFrontiers in Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsPsychologyStudent engagementQuarter (Canadian coin)OddsEducational attainmentDevelopmental psychologyPedagogySocial psychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Understanding what different stakeholders mean by “parental engagement” is vital as school leaders and policy makers increasingly turn to parental engagement to improve pupils’ outcomes. Yet, to-date, there has been little examination of whether parents’, teachers’, and school leaders’ conceptions of parental engagement match those used in research and policy. This case study used online questionnaires to explore the conceptions of parental engagement held by 103 parents and 40 members of staff at one large English primary school. The results showed that only a quarter of school staff conceptualized parental engagement in relation to learning at home and that school leaders appeared to overestimate the impact of school-based activities. This is at odds with previous research suggesting that it is parental engagement with learning in the home – rather than parents’ involvement with school - that is associated with pupil attainment. This suggests that there might be a striking mismatch in the way that parental engagement is conceptualized by researchers advocating for its efficacy, and by school staff devising and implementing parental engagement initiatives. It is vital to raise awareness of this possibility amongst practitioners, researchers, and policy makers because any such mismatch could result in the misdirection of time and resources and the undermining of parental engagement’s potential as a powerful tool for raising attainment and closing achievement gaps.

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)0.001
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 score0.785
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.044
GPT teacher head0.352
Teacher spread0.308 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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