University for All Programs (ProUni): Engagement, Satisfaction, and Employability
Bibliographic record
Abstract
A training at Higher Education level needs, in addition to improve the skills specific in the area chosen, to develop a set of skills and/or personal attributes that make him or her more likely to succeed in the profession. In this context, this paper was developed and has the objective to identify the relationships between engagement, satisfaction, and employability of students who completed the university as University for All Program (ProUni) scholarship. This target group of students was chosen because of the importance of ProUni for the Advancement of Education policies of affirmative actions in Brazil. The ProUni gives scholarships to students from minority (underrepresented) groups to study at private universities, through the National Secondary Education Exam – ENEM (Brasil, 2005). Among these groups are students who attend public high schools (a proxy for lower social class), low-income students, African Brazilian students, Indigenous Brazilian students, students with disabilities, and not graduated teachers that work in public elementary and secondary schools. The research involved 198 ProUni graduates invited to answer an online questionnaire. There were 134 respondents, 123 (91.8%) were working since we were interested in employment, only these participants were included in the analysis. The results suggest that employability consolidates and reflects in the conquest of labor activity, as well as in graduate satisfaction with their training and job. These results are indicative of the engagement of the student with their learning, therefore with their graduate degree.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".