Changing the Educational Culture of the Home to Increase Student Success at School
Bibliographic record
Abstract
Parent involvement in their children’s learning is widely acknowledged as having a positive effect on student academic success. Of particular relevance is the finding that the influence of parent engagement can mitigate differences in socioeconomic status (SES) and family background. Family background is a multi-dimensional concept that includes the family’s “educational culture” (including for example, parenting style, parental expectations for children’s work at school, direct instructional support for school learning, active parent interest in the school’s curriculum, and the monitoring of children’s engagement with their school work). It is these features of a child’s home environment that directly influence much of the social and intellectual capital students need to be successful at school. This paper summarizes a quasi-experimental field study which explored the relative effects of alternative types of school interventions on parent engagement. All of these interventions aimed to further engage parents in the education of their children as a means of both improving student achievement and closing gaps in achievement for students living primarily in challenging social and economic circumstances. Initiatives by school staffs aimed at helping those families struggling to build productive educational cultures in their homes would appear to be a very promising strategy for closing achievement gaps between advantaged and disadvantaged students. The study provides eight lessons other districts might take heed of as they embark on their own parent engagement interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".