Enrollment in Distance Education Classes is Associated with Fewer Enrollment Gaps Among Independent Undergraduate Students in the US
Bibliographic record
Abstract
The purpose of this research is to determine whether nontraditional undergraduate students in the US who enroll in distance education classes are less likely to have an enrollment gap (enrollment gap=part year enrollment). Previous research has shown that preference for distance education classes is significantly greater among nontraditional than among traditional undergraduate students; nontraditional students invariably have a greater number of competing demands (work and family) on their time. Since distance education courses provide students with more convenient and flexible class schedules, nontraditional students, who have time or location constraints that prevent them from enrolling in face-to-face classes during a semester or quarter, may be more likely to enroll in distance education classes in order to stay enrolled for the entire academic year. Based upon this rationale, we predicted that enrollment in distance education classes is significantly related to a decreased likelihood of an enrollment gap among nontraditional students. To test this prediction, we used data from the National Postsecondary Student Aid Survey (NPSAS) conducted in 2008. The NPSAS 2008 used a complex survey design to collect data from a nationally representative sample of about 113,500 postsecondary undergraduate students in the US. Results confirm our prediction, and show that enrollment in distance education is significantly related to a decreased likelihood of an enrollment gap among nontraditional students, but not among traditional students. Results also show that five of the seven dropout risk factors (identified by previous research to decrease 6-year graduation rates) are each significantly associated with an increased likelihood of an enrollment gap. These results suggest that the offer of distance education classes could increase degree progress and possibly completion rates for nontraditional undergraduates who are at high risk for dropout.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".