Empirical Analysis of Return to Distance Higher Education in Different Disciplines
Bibliographic record
Abstract
Few empirical studies have analyzed the return to distance higher education in different academic disciplines. This study used quantitative methods, data from a nationwide survey, and Mincerian earnings function to analyze the return to distance higher education among different disciplines in China’s labor market. Results were compared with the return to face-to-face higher education and showed that the returns to face-to-face higher education were higher than those to distance higher education. Returns to the disciplines of economics and management were at a high level in both face-to-face and distance education; returns to the disciplines of literature, as well as education and law, were at a low level in both face-to-face and distance education. The returns to the disciplines of science and engineering were higher in face-to-face education than in distance education. This paper proposes several recommendations. Adults who do not have higher education degrees should invest in distance higher education to obtain considerable monetary returns, particularly in the disciplines with higher returns such as management and economics. China’s distance education institutions should improve the quality of teaching in science and engineering education and find ways to provide high-quality experimental teaching practices. At the same time, they should scale back on instruction of literature, as well as education and law.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".