First generation college students and non-first-generation college students: Perceptions of belonging
Bibliographic record
Abstract
Background and objective: First generation college students (FGS), are emerging as an important demographic group for colleges and universities. Having a ‘sense of belonging’ or belonging is important to the success of all college students, especially for the retention of students who may be at risk of not completing their academic degree. The purpose of this study is to analyze differences between first generation and non-first-generation college students based on a mattering survey.Methods: Two hundred and thirty undergraduate students in one New England College participated in a study designed to uncover differences between FGS and non-first-generation college students based on responses to a mattering survey. The study utilized a questionnaire designed to capture students’ opinions on mattering. Open questions were included to inform and enrich the data.Results: First-generation student perceived a greater number of obstacles in their college experience than non-first-generation students. Obstacles to academic success for first generation students included lack of time to study due to work and family responsibilities, financial struggles, and unhelpful faculty relationships. Facilitators were helpful faculty and peer relationships. Lack of mental health support was cited as a barrier to both cohorts of students.Conclusions: Having a sense of belonging or mattering can help the student feel a connection to the college. This may aid the student in persisting towards graduation. Persistence toward graduation is important for all students including nursing 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".