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Record W1944732043 · doi:10.24059/olj.v16i1.175

Enrollment in Distance Education Classes is Associated with Fewer Enrollment Gaps Among Independent Undergraduate Students in the US

2012· article· en· W1944732043 on OpenAlexaboutno aff
Manuel Pontes, Nancy Marie Hurley Pontes

Bibliographic record

VenueOnline Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)Distance educationTest (biology)PsychologyMathematics educationMedical educationHigher educationQuarter (Canadian coin)PreferenceMedicineMathematicsPolitical scienceStatisticsGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.019
GPT teacher head0.390
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2012
Admission routes1
Has abstractyes

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