School Engagement as Predicted by Future Orientation and Academic Self-Efficacy
Bibliographic record
Abstract
Objective: This study aimed to investigate the predictive roles of future orientation and academic self-efficacy in determining school engagement among Canadian high school students. Methods and Materials: The research employed a correlational descriptive design with a sample of 323 students selected based on Morgan and Krejcie’s sampling table. Participants were recruited from various high schools across Canada and completed three standardized instruments: the School Engagement Scale (Fredricks et al., 2005), the Future Orientation Scale (Steinberg et al., 2009), and the Academic Self-Efficacy Scale (Zimmerman et al., 1992). Data analysis was conducted using SPSS version 27. Descriptive statistics were used to report means and standard deviations of study variables. Pearson correlation analysis was used to examine the relationship between school engagement and each of the two predictor variables. Multiple linear regression analysis was then conducted to determine the extent to which future orientation and academic self-efficacy predict school engagement. Findings: The results indicated that both future orientation (r = .46, p < .001) and academic self-efficacy (r = .59, p < .001) were significantly and positively correlated with school engagement. The multiple regression model was statistically significant, F(2, 320) = 84.55, p < .001, with an R² of .40, indicating that 40% of the variance in school engagement could be explained by the two predictor variables. Academic self-efficacy (β = .43, p < .001) emerged as a stronger predictor than future orientation (β = .25, p < .001), although both variables made meaningful contributions to the model. Conclusion: The findings highlight the importance of enhancing both future orientation and academic self-efficacy in adolescents as a means to foster greater school engagement. These results provide valuable insights for educators, counselors, and policymakers aiming to support academic motivation and reduce disengagement among high school students.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".