An Online Doctorate for Researching Professionals: Program Design, Implementation, and Evaluation
Bibliographic record
Abstract
The model of an online professional doctorate that we present in this book is based on our experience with designing and implementing the online EdD degree in educational technology offered at the University of Florida.The journey began in 2006, when Kara and her colleague Rick Ferdig (now at Kent State University) began to recognize that our university's PhD program in education did not meet the needs of all students.Like most PhD programs, ours was oriented toward students who aimed to work in an academic environment, teaching and conducting research, yet a significant number of our doctoral candidates had no interest in an academic career.Many were already working professionals whose goal was to apply their new knowledge in a particular context or, in some cases, simply to strengthen their credentials.These students typically attended school part-time, often commuting to campus after work, and took years to finish the program.We made accommodations for them as best we could, by adjusting course assignments to better meet their needs and by working out schedules that allowed them to pursue their degree on a part-time basis.Despite these accommodations, however, the PhD program was an awkward fit for them.We had also begun receiving numerous inquiries from working professionals elsewhere in the country who were interested in earning a doctoral degree while maintaining their existing jobs.Although such an arrangement was clearly incompatible with our campus-based PhD viii
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".