THE IMPACTS OF MENTORSHIP ON DUAL ENROLLMENT HIGH SCHOOL STUDENTS
Bibliographic record
Abstract
"Dual enrollment programs enable high school students to take community college courses and earn high school and college credits, saving two years of college expenses. However, many dual enrollment students lack a robust support system for success in college-level coursework and environment. The authors created an interdisciplinary mentorship program that pairs a volunteer dual enrollment senior student with a dual enrollment junior student in a longitudinal mentoring relationship to address this. This study examined mentors’ and mentees’ long-term evaluation of the program and its impacts. Thirty-nine mentors and mentees were randomly matched with a waitlist control group, and mentoring relationships lasted for a full academic quarter. Participants later completed an anonymous online feedback survey (based on the Likert Scale), with a response rate of 67% (n = 26). Mentees reported an average 1.37 Likert scale increase in their comfort in dual enrollment; mentors reported an average 2.43 Likert scale increase in confidence in teaching others. Mentees’ comfort in the college environment increased with the frequency of meetings (p<0.05); the number of meetings did not correlate to their grade point average (GPA) (p>0.05). Change in dual enrollment comfort was more significant among matched students than waitlisted (p<0.05). Notably, many dual enrollment programs have a ~10% student academic probation rate (GPA<2.0) each quarter; none of the mentees experienced academic probation, but this was not significant. Among mentees, 79% reported interest in being a mentor the following year. These results indicate that peer mentorship is crucial for dual enrollment student success and presents a self-sustaining model for the future."
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".