From Storytime to Success: Prospective Longitudinal Associations Between Toddler Literacy Enrichment and Long-Term Student Engagement in a Millennial Birth Cohort of Boys and Girls
Bibliographic record
Abstract
Cross-sectional research suggests associations between enrichment and cognitive skills in toddlerhood. There are no prospectively designed longitudinal studies that investigate the link between early home literacy activities and subsequent mechanisms that explain the putative cognitive benefits. This study tests long-term associations between toddler literacy enrichment and later student engagement across key academic transitions, from kindergarten to the end of high school. Using the Quebec Longitudinal Study of Child Development (QLSCD) population-based birth cohort data, we examined whether parent-reported experiences of shared reading, looking at picture books or illustrated stories, and pretend writing at age 2 years predict later teacher- and self-reported student engagement at ages 6, 12, and 17 years. The results from multiple regression models, stratified by sex and adjusted for pre-existing and concurrent child and family characteristics, revealed significant associations between early literacy enrichment and later engagement. For boys and girls, literacy enrichment in toddlerhood predicted increases in classroom engagement from kindergarten to the end of high school. These findings highlight the lasting influence of early literacy exposure on subsequent learning-related behaviors, both in and beyond the classroom. They underscore the importance of promoting enrichment in early childhood as a family strategy toward individual readiness to learn, a cornerstone of crystalized intelligence.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".