Understanding the Determinants of K-12 Academic Success in the United States: OLS Regression
Bibliographic record
Abstract
This paper investigates the critical factors that influence K-12 students' academic performance in the United States. Utilizing the 2019 Parent and Family Involvement (PFI) in Education Survey data collected by the National Center for Education Statistics (NCES), three new indices are constructed that summarize parental involvement in school and family events, parental satisfaction with schools, and students' extracurricular learning time. Initially, data visualization is employed to examine the data characteristics, followed by Ordinary Least Squares (OLS) regression to analyze the relationship between students' grades and these three indices. The findings reveal a significant positive correlation between students' grades and the extent of parental involvement in activities, parents' satisfaction with school work, and students' studying time outside the classroom. These results can provide valuable and actionable insights for future educational practices and policies while guiding parental involvement to support their children's academic achievement.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Regression analysis of parental involvement and K-12 academic performance; the object is educational achievement.
This study models determinants of K-12 academic performance, not the practice of research.
Education research on parental involvement and K-12 grades; schooling outcomes, not research systems.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".