Teaching Learning and Motivation Strategies to Enhance the Success of First-Term College Students
Bibliographic record
Abstract
This study examined the effect of taking a Learning and Motivation Strategies course on GPA and retention of 351 new freshmen over their first four quarters, in comparison to 351 matched non-takers. The course taught four strategies and eight sub-strategies to help students overcome procrastination, build self-confidence, take responsibility, learn from lecture and text, write papers and manage their lives. New freshmen who took the course in their first quarter had significantly higher GPAs in each of their first four quarters, significantly higher retention (six times more likely to be retained) than did matched controls, and had higher graduation rates. Purposes of the Study Getting into college and then dropping out is a problem at postsecondary education institutions, even among students who enter with high school records that would appear to predict college success. On a national basis the university drop-out rate is about 25 % and community college drop-out rate 50%, with the majority in both places occurring in the first year. Among urban minority students who enroll in college, 55 % choose community colleges, often because of their easy accessibility, low cost, broad based admission policies, and diversity of program offerings, yet only 50 % remain in school (American Association of Community
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".