The Effects of Formal Mentoring on First-Time Freshman GPA, Course Completion, and Retention Rates
Bibliographic record
Abstract
Problem The purpose of this study is to evaluate the effect of one quarter of a formal mentoring program on the academic success and retention of first-year university students. Method Prior to the beginning of fall quarter 2007, a randomly selected group of 75 first time freshmen students at Walla Walla University (WWU) were invited to participate in an experimental mentoring program. Similarly, a silent control group of 75 first-time freshmen students were randomly selected. Non-student mentors were interviewed and hired to carry out the mentoring project. The retention rate, grade point average (GPA), and credit drop/failure rate of the students in the experimental group were compared to the retention rate, GPA, and credit drop/failure rate of the control group. Results The results indicated that though there was not a significant difference in the overall mean university GPA between the two groups, fewer students in the mentored group ended the quarter with a university GPA below 2.0 than did students in the control group. Additionally, mentored students who entered the university with low high-school performance indicators showed stronger academic success than did control group students. And students in the mentored group dropped and/or failed fewer credits than did those in the control group. There were no differences in retention rate. Conclusions A longitudinal study is needed to give closer study to the extended impact of mentoring on student success and retention.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".