Effect of Peer-Mentoring Programs on Academic Motivation and School Belonging in First-Generation Learners
Bibliographic record
Abstract
This study aimed to examine the effectiveness of a peer-mentoring program on enhancing academic motivation and school belonging in first-generation learners. A randomized controlled trial design was used with 30 first-generation students from Canada, randomly assigned to either an intervention group (n = 15) that participated in an eleven-session peer-mentoring program or a control group (n = 15) that received no intervention. The sessions, conducted weekly over three months, focused on academic goal setting, motivation, school engagement, and emotional support. Data were collected at three time points—pre-test, post-test, and five-month follow-up—using the Academic Motivation Scale and the Psychological Sense of School Membership scale. Repeated measures ANOVA with Bonferroni post-hoc tests were conducted in SPSS-27 to analyze group differences over time. The intervention group showed significant improvements in both academic motivation and school belonging across time compared to the control group. Repeated measures ANOVA revealed a significant interaction effect between time and group for academic motivation (F(2, 56) = 28.42, p < .001, η² = .49) and for school belonging (F(2, 56) = 27.35, p < .001, η² = .47). Bonferroni post-hoc tests confirmed significant increases from pre-test to post-test and pre-test to follow-up for both variables in the intervention group (p < .001), with no significant change between post-test and follow-up, indicating sustained effects. The control group showed no significant changes over time. Peer mentoring significantly improves academic motivation and school belonging among first-generation learners, and these gains are sustained over time. Structured, relational interventions that emphasize shared experience, goal setting, and emotional support can play a key role in fostering educational engagement and persistence in marginalized student populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".