How parents contribute to children’s science skills? Examining the link between home science leaning ecosystem and children’s science achievement
Bibliographic record
Abstract
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".