Four supervisory mentoring practices that support online doctoral students’ academic writing
Bibliographic record
Abstract
Academic writing in both face-to-face and online environments is often fraught with tension, emotion, and challenge. The quality of doctoral students’ online academic writing experiences can be a difference maker in the successful completion of programs. This study examines how mentoring practices support online doctoral students’ academic writing, building on prior research that identified five enabling factors of effective online doctoral supervision, with a focus on cultivating a collaborative online community of support for academic writing. Using a comparative case study approach, interviews with five recently completed faculty of education doctoral graduates at a large university in western Canada were analyzed to identify four mentoring supervisory practices that support online doctoral students’ academic writing: (a) fostering a trusting, supportive community of practice; (b) engaging in regular synchronous meetings combined with iterative cycles of mentoring and scaffolding; (c) using coursework and program structures as a springboard for writing; and (d) providing diverse models of academic writing. Central to the effectiveness of the four online supervisory mentoring practices was the notion of trust which enabled students to develop their academic writing skills, scholarly identities, and successfully complete their doctoral degrees. This study is significant for identifying supervisory mentoring practices that led to students’ sense of gratitude and flourishing, further highlighting how crucial relational trust is for online doctoral students’ academic writing.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".