Retention Rates for the First Three Years of a Linked-courses Learning Community
Bibliographic record
Abstract
While enrollments in computing degrees and courses have grown rapidly in the past decade, both female and minority male students remain underrepresented in computing programs. This makes recruitment and retention of these populations a continuing concern. To attempt to address the issue at our institution, we created a linked-courses learning community targeting females and minority males enrolled in several computing majors. Here we present retention rates for the first three years of the linked-courses learning community. The results show that the learning community appeared to make a difference for some cohorts, improving their retention rate and academic performance over comparable institutional populations. Unfortunately, the more challenges a cohort faced in terms of factors that contribute to a difficult transition to college, the less the learning community was able to overcome these challenges. There were also other differences between the cohorts, as seen in attitudes measured by pre- and post-quarter surveys, that complicate generalizations about the impact of the learning community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".