Bibliographic record
Abstract
Estimates in the literature for within-year retention at 2-year colleges range from 57% to 83.9%. This indicates that a large proportion of students who attend 2-year colleges may not be retained beyond the first semester of their studies. Attrition potentially represents a major loss to the student, to the institution, and to society. With current accountability and funding realities becoming more openly discussed, Canadian colleges may not be able to afford to ignore their high rates of attrition in the future. The focus of this research was to estimate the rate of within-year retention among a sample of students attending two comprehensive community colleges in western Canada, and to develop a predictive model that identified potential determinants of retention among these students. Retention was examined among the total sample, among the sample from each college separately, and among the sample enrolled in each credential type. Astin’s Input-Environment-Output model was used as the framework for this research. The model purports that institutional outputs such as retention must be evaluated in the context of the original student inputs and ongoing environmental factors. Multivariable logistic regression was used to develop predictive models of college student retention. The estimated overall retention rate among this sample was 83.6%, although differences were observed by credential type. Among the aggregate sample, two environmental factors - grade point average and credit load - were the strongest predictors of retention once other factors were considered. The predictors of retention differed by credential type. The results indicate that the greatest gains in retention may be realized by strategies aimed at encouraging full-time enrolment and supporting academic achievement. The results of the current study suggest that sub-groups may exist for whom retention is predicted by unique factors. It is important that retention be examined on an institution-by-institution basis. Enhancing our understanding of Canadian college student retention, and taking action to improve retention, may contribute to Canada’s future prosperity in a knowledge economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".