Exploring Trends in BIPOC Student Engagement: A Review of the Literature
Bibliographic record
Abstract
The literature has shown that student engagement (SE) improves students’ self-confidence, problem-solving, and interpersonal skills, leading to positive outcomes in overall experience in higher education and potential for graduation and career success. However, SE in higher education is not well-researched among students who identify as Black, Indigenous, and People of Colour (BIPOC) or within the intersection of students with these identities who are also first-generation or international students in the Canadian context. This literature review investigates factors for differing rates of SE among BIPOC students compared to their White peers in Canada. Unfortunately, due to the limited Canadian-specific literature, we could not solely include Canadian studies and also included US studies in our results. Findings suggest that greater representation of BIPOC faculty contributes to a sense of belonging for BIPOC students, which can increase student engagement. BIPOC, international and first-generation students are likelier to work off-campus, making it challenging to participate in SE. Lastly, BIPOC students are more likely to be commuter students, creating an inverse correlation between public travel and SE. Our findings show that hiring more BIPOC faculty, creating more opportunities for on-campus work, and creating more opportunities for alternative transit are potential next steps to aid the SE gap between BIPOC and White 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".