Increasing Access to Higher Education: A study of the diffusion of online teaching among 913 college faculty
Bibliographic record
Abstract
Online learning environments provide an unprecedented opportunity to increase student access to higher education. Accomplishing this much needed goal requires the active participation and cooperation of university faculty from a broad spectrum of institutional settings. Although online learning has seen rapid growth in recent years, it remains a relatively small percentage of the entire curriculum of higher education today. As a relatively recent development, online teaching can be viewed through the lens of diffusion of innovation research. This paper reports on research from 913 professors from community colleges, four-year colleges, and university centers in an attempt to determine potential barriers to the continued growth in adoption of online teaching in higher education. It is concluded through factor and regression analysis that four variables are significantly associated with faculty satisfaction and their likelihood, therefore, to adopt or continue online teaching – these include levels of interaction in their online course, technical support, a positive learning experience in developing and teaching the course, and the discipline area in which they taught. Recommendations for institutional policy, faculty development, and further research are included. Keywords: online teaching, faculty satisfaction, faculty development, diffusion of innovation, access, higher education, study
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".