Improving Retention and Enrollment Forecasting in Part-Time Programs.
Bibliographic record
Abstract
INTRODUCTION This article describes a model that can be used to analyze student enrollment data and can give insights for improving retention of part-time students and refining institutional budgeting and planning efforts. Adult higher-education programs are often challenged in that parttime students take courses less reliably than full-time students. For many institutions, part-time adult students are also less likely to graduate and complete a credential program. Much has been written about how to improve part-time adult student retention, but much less has been done to predict students most at risk of dropping out. Studies that have explored the likelihood of dropping out have tended to focus on student characteristics such as race, sex, income, and prior achievements such as grades or scores on entrance exams. The model presented in this article is unique in that it “de-cohortizes” student enrollment data and then uses students’ own enrollments as a predictor of future enrollments. The benefits of such a model are twofold. First, students’ past enrollments are, in fact, predictive of future enrollments. Second, insofar as students’ enrollment patterns are constantly changing each quarter, the predictive power of the model increases over time for each student. Such is not the case for models that examine only student characteristics, most of which do not change throughout the course of a student’s academic career.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".