A LATENT GROWTH MODELLING APPROACH TO INVESTIGATING GENDER DIFFERENCES IN THE DEVELOPMENT OF BEHAVIOURAL SELF-REGULATION AND ACADEMIC OUTCOMES FROM KINDERGARTEN TO GRADE 2
Bibliographic record
Abstract
A growing body of literature indicates the behavioural aspect of self-regulation, including paying attention, remembering instructions, controlling impulses and directing one’s action, amidst environmental distractions, is a critical component of successful school functioning. Some studies have identified differences between male and female students’ behavioural self-regulation abilities; however, it is unclear whether improvements in this ability are influenced by gender and whether they parallel growth in the development of reading and math abilities. The present study explored gender differences in the development of children’s behavioural self-regulation from kindergarten to Grade 2, using a direct assessment. Longitudinal associations between behavioural self-regulation and early reading and math skills were also examined. A latent growth modelling approach was utilized for the analyses across three waves of data collection. The study participants included 197 children (106 males and 91 females). On average, children were approximately five years old at the start of the study (M =5.39, SD=0.593). The study results revealed gender similarities in behavioural self-regulation growth, positive correlated initial skill levels and rates of growth between behavioural self-regulation and reading, and positive correlated initial skill levels between behavioural self-regulation and math. Implications for future research, as well as educational policies and instructional practices, are discussed.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".