Longitudinal associations between gaming and academic motivation during middle childhood
Bibliographic record
Abstract
Abstract Background Child video game playing (“gaming”) may lead to decreased child academic motivation. Conversely, children with low academic motivation may seek fulfillment through gaming. We examined bidirectional associations between child gaming and academic motivation across middle childhood. Methods Our analyses are based on 1,631 children (boys = 785) followed in the context of the Quebec Longitudinal Study of Child Development. Data on gaming and academic motivation were collected repeatedly at ages 7, 8, and 10. Measures of child gaming were parent-reported and reflect daily video game playing time. Measures of academic motivation were child self-reported and reflect enjoyment in learning mathematics, reading, and writing. To disentangle the directionality of associations, we estimated a random-intercept cross-lagged panel model to estimate bidirectional, within-person associations between gaming and academic motivation in a cohort of school-aged Canadian children. Results Our results revealed unidirectional associations whereby more frequent gaming by boys at age 7 years predicted lower academic motivation at age 8 years ( β = −.11, 95% confidence interval [CI]: −.22 to −.01), and similarly, gaming by boys at age 8 years predicted lower academic motivation at age 10 years ( β = −.10, 95% CI: −.19 to −.01). Changes in boys’ academic motivation did not contribute to subsequent changes in gaming. There were no associations between gaming and academic motivation for girls. Conclusions More time devoted to gaming among school-aged boys is associated with reduced academic motivation during a critical developmental period for the development of academic skills. Fostering healthy gaming habits may help promote academic motivation and success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".