School Engagement as Predictors of Academic Achievement of the Left-Behind Children in Henan Province, China
Bibliographic record
Abstract
With the expansion of industrialisation and rapid development of economy in China, migration of the rural population to the urban areas in seek of employment has resulted in changes in the family structure and increased number of left-behind children (LBC) in China. Numerous studies on LBC have concluded the critical issues among the children related to their overall well- being and their performance at school. The current study aimed to investigate the impact of emotional, cognitive and behavioural engagement of the LBC on the academic achievement of the left behind children. Quantitative research method was used and the sample were 186 left- behind children in Henan Province, China. To measure school engagement, the instrument developed by Finlay (2006) was used. Correlation and hierarchical multiple regression analyses were conducted to examine the relationship between the various predictors and academic achievement. The findings concluded that emotional, cognitive and behavioural engagement of the LBC at school were significantly correlated with academic achievement. The hierarchical regression findings concluded that emotional engagement (β= .268; t= 3.593, p<0.000, cognitive engagement (β= .245; t= 3.284, p<0.000 and care takers (β= .132; t= 2.009, p<0.05) were significant predictors of academic achievement of the LBC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".