Experiences of Graduate Students in a Blended Professional Doctoral Degree: Perspectives from Educational Leadership
Bibliographic record
Abstract
The landscape of doctoral education has evolved to deliver degree programs that meet the diverse needs of students, in particular working professionals. Using a narrative inquiry approach informed by Clandinin (2006, 2007), the experiences of three students in a blended professional Doctor of Education program, along with their common academic supervisor, were examined. As there are often incongruencies between expectations and realities, elements of the EdD program (cohort structure, two-week annual residencies, and online courses) were considered as well as the reasons for pursuing doctoral work were explored. Data generation, collection, and analysis moved from individual to collaborative work within the research team. The study was conducted with the aim to elevate within the literature the experiences of doctoral students in blended and online programs as well as to share recommendations for supportive learning and supervisory experiences.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".