Distance Education and Horizontal Stratification in U.S. Higher Education
Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».