The Role of Home Literacy Environment in Reading Beyond the Early Grades
Bibliographic record
Abstract
Most studies on home literacy environment (HLE) have examined its role in children’s language and literacy skills in the early childhood years. However, parents continue to be involved in their children’s learning later on. Thus, in this study we aimed to examine if different HLE aspects (code-related HLE, meaning-related HLE, access to literacy resources, age of onset of parent-child literacy activities and child’s own independent reading) predict children’s reading and spelling performance in upper grades. Study 1 was conducted in China and included 111 Grade 3 children (57 girls, 54 boys; Mage = 9.22 years, SD = 0.30) and their parents. Study 2 was conducted in Cyprus and included 208 Grade 4 Greek-speaking children (114 girls, 94 boys; Mage = 9.77 years, SD = 0.39) and their parents. Results of hierarchical regression analyses in both studies showed that after controlling for the effects of parents’ education and nonverbal IQ, neither code- nor meaning-related HLE activities predicted reading. In addition, they showed that access to literacy resources was a significant predictor of both reading outcomes in Study 1 and child’s independent reading was a significant predictor of reading and spelling in Study 2 (with one exception when predicting passage comprehension). Taken together, these findings suggest that HLE might be important in children’s reading and spelling performance, but different aspect of HLE play a role at different times. When children are in upper grades, parents may still contribute to their children’s literacy skills by providing access to literacy resources and by creating an environment that allows their child to practice independent reading.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".