Doctoral student reflections of blended learning before and during covid-19
Bibliographic record
Abstract
Purpose: Our study aimed to address the central research question: how were our experiences as graduate students in a blended learning professional doctoral program changed by the COVID-19 crisis? The study adds to a growing body of literature on blended learning graduate programs. Methods: We employed action research as our central methodology and leveraged narrative inquiry to elevate our (students’) voices. The two participant-researchers responded to a series of questions supported by narrative reflections from their common academic supervisor. Emergent themes were identified in the data using narrative analysis techniques for coding qualitative data into themes. This was followed by a second phase of data collection and analysis after the emergence of the COVID-19 pandemic. Results: The researchers identified four themes within the data: 1. balancing doctoral work with professional and personal responsibilities; 2. cohort provides formal and informal support; 3. individuality of the experience; and 4. supervisory group support. Implications: Our study offers a number of key learnings that may benefit researchers studying blended learning programs. The key learnings suggest benefits to cohort-based, blended learning programs, as well as difficulties that may emerge in the individuality of the experience, when encountering crises, as well as more generally. (DIPF/Orig.)
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".