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Record W7113405440

Distance Education and Horizontal Stratification in U.S. Higher Education

2019· dissertation· en· W7113405440 on OpenAlexaboutno aff

Bibliographic record

VenueDigiNole (Florida State University) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationHigher educationBivariate analysisQuarter (Canadian coin)RevenuePostsecondary educationGeographical distanceStatistics educationSet (abstract data type)
DOInot available

Abstract

fetched live from OpenAlex

Purpose: Distance education has become an increasingly common mode of instruction in U.S. higher education, and today more than a quarter of students are enrolled in distance courses or programs that are fully online. This dissertation asks two fundamental questions related to the growing presence of distance-based instruction in U.S. higher education. First, does increased college access in the form of distance enrollments contribute to horizontal postsecondary stratification? Second, is the adoption of distance education indicative of academic capitalism? I make use of two broad theoretical perspectives to frame my analysis and develop a set of hypotheses concerning the types of colleges and universities that enroll greater percentages of undergraduates in at least one distance course or completely online degree programs. Drawing on the “effectively maintained inequality” (EMI) perspective, I hypothesize that enrollment in distance courses and programs will be higher at less selective colleges and universities and will vary by institutional sector. Regarding sector, I hypothesize that distance enrollments are highest at for-profit institutions, and higher at public institutions than at private. Based on the “academic capitalism” perspective, I hypothesize that institutions with lower levels of financial resources will rely more heavily on distance education as a revenue source and a means of reducing costs. Methods: I test these hypotheses using the NCES Integrated Postsecondary Education Data Set for 2015-16. The sample of consists of 2,180 four-year postsecondary institutions. Hypotheses are tested using one-way and two-way ANOVA models and bivariate correlation analyses. Results: Enrollment in distance education varies significantly by level of selectivity, sector, and financial resources. As hypothesized, less selective institutions have significantly higher percentages of distance enrollment, but interesting subtleties emerge between sectors. Within public institutions distance course and program enrollment are fairly steady across selectivity levels, while enrollment differs substantially among private colleges and universities. Additional analyses of student composition by sector and selectivity confirm that social inequalities by race and class are not likely diminished by distance education. Institutions with fewer resources and expenditures have higher levels of distance education, as expected. Specifically, private institutions with fewer financial resources have greater distance course and program enrollment, and for-profits with fewer resources have greater distance program enrollment. However, overall revenue and expenses are not related to distance enrollment among public universities. Exploratory analysis of detailed revenue and expenditures paint a more nuanced picture of the financial resources that vary with greater reliance on distance courses and programs. Conclusion: The growth of distance enrollments does not reduce social stratification in higher education because distance enrollment growth is occurring disproportionately at less selective private and for-profit colleges and universities—institutions that are costlier to attend, have lower economic payoffs, and disproportionately enroll students of color and lower income college students. The growth of distance enrollment is also consistent with depiction of higher education as an academic capitalist regime, in that distance enrollment appears to function as a revenue source for colleges and universities, particularly for those outside the public sector.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.304
Teacher spread0.287 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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