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Record W7161153039 · doi:10.17605/osf.io/hz7kt

How parents contribute to children’s science skills? Examining the link between home science leaning ecosystem and children’s science achievement

2025· other· W7161153039 on OpenAlexaboutno aff
Anna Jia-Jun Zhang, Tomohiro Inoue

Bibliographic record

VenueOpen MIND · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationScientific literacyReading (process)Science educationScience learningLiteracyScientific literatureScience communication

Abstract

fetched live from OpenAlex

Despite a wealth of research showing the influences of home learning environment on children’s literacy and numeracy, less attention has been given to its role in science domain. The concept of home science environment (HSE) was introduced by Korpan and colleagues in 1997, encompassing parents’ engagement in activities such as reading scientific concepts, assisting with science experiments, and supporting observations. However, only a handful of studies have examined the role of HSE, and they were predominantly in Western societies, such as Canada, Germany and the United States (Junge et al., 2021; Korpan et al., 1997; Westerberg et al., 2022). These existing studies also had inconsistencies in their conceptualization of HSE factors. In addition, while three key areas of science, namely scientific core ideas (or scientific content knowledge), crosscutting concepts, and scientific practice (or inquiry) have been identified (Greenfield et al., 2017), most studies have primarily focused only on the scientific core ideas domain, resulting in a less attention to the other two areas (e.g., Junge et al., 2021; Westerberg et al., 2022). To comprehensively understand the environmental correlates of children’s science skills, it is important to include all three domains in the same model. Furthermore, the majority of existing studies have focused solely on the proximal factors (e.g., home learning environment) rather than employing a holistic approach that captures both proximal and distal characteristics (e.g., culture capital, parents’ education, parents’ expectations) to understand parental influences on children’s science learning (Gregoriadis & Evangelou, 2022). To address these gaps, we aim to examine (a) the factor structure of HSE (b) the relationships among family cultural and social capital, parents’ education, parents’ expectations, HSE and children’s science skills (scientific core concepts, inquiry of practice, crosscutting scientific thinking) in Grade 8 children in China, utilizing a large-scale dataset. Drawing on the previous studies (Junge et al., 2021; Vandermaas-Peeler et al., 2016), we hypothesize that (a) HSE would exhibit a multidimensional factor structure with distinct factors related to parental direct involvement (Liou et al., 2019; Zafeer et al., 2025), resources (Jiang, 2020; Morgenthaler,2021), and indirect teaching opportunities(Haden, 2010; Vandermaas-Peeler et al., 2016), and (b) different dimensions of the HSE would be associated with all three dimensions of science skills (core concept, inquiry of practice, crosscutting scientific thinking). In addition, we expect a serial mediation effect, in which cultural and social capital and parents’ education would have an indirect effect on children’s science skills through HSE factors and parents’ expectation. This finding will contribute to our understanding of how specific components of the home science learning ecosystem are associated with science achievement in Chinese middle school children.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.295
Teacher spread0.270 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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