Against the Odds: The Impact of the Key Communities at Colorado State University on Retention and Graduation for Historically Underrepresented Students.
Bibliographic record
Abstract
Learning communities are a high impact activity that can influence students’ likelihood for success. Colorado State University (CSU) created the Key Communities (Key) program, which is open to all students but targets students that have persistently lower graduation and retention rates. The majority of Key students are under-represented (ethnically diverse, low-income, and/or first generation to college) and/or students with lower levels of academic preparation. This paper describes the structure and purpose of Key and shares the results of an institutional level assessment of Key’s impact on graduation and retention. Since participation in Key is not randomly assigned, this analysis utilizes propensity score matching to estimate Key’s treatment effect. Results show that Key has a positive impact on graduation and retention for all students, but Key is incredibly effective for students who come to CSU with characteristics that have historically put them at risk for attrition. Tae Nosaka is the Director of the Key Communities and University Learning Communities Coordinator at Colorado State University in Fort Collins, CO. Heather Novak is a research analyst in the Office of Institutional Research at Colorado State University.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".