Cultivating a Continuous “I Don’t Know”: Four Supervisory Mentoring Practices that Support Online Doctoral Students’ Academic Writing
Bibliographic record
Abstract
<title>Abstract</title> Academic writing in both face-to-face and online environments is often a challenging experience for many faculty and students and can be fraught with tension and emotion. Thus, the quality of doctoral students’ online academic writing experiences can be a difference maker in successful completion of programs. Building on our earlier work identifying five enabling factors of successful online doctoral supervision, this study explores practices that enable factor five: Cultivating a collaborative online community of support for academic writing. Using a comparative case study approach, we analyzed the data from interviews with five recently completed doctoral graduates to determine the mentoring practices that cultivated for them, effective online doctoral student academic writing relationships. Findings identified four supervisory practices: (a) engaging in regularly scheduled meetings with iterative cycles of mentoring and scaffolding; (b) engaging students in a trusting, supportive community of practice; (c) using coursework and program structures as a springboard for writing; and (d) providing diverse models of academic writing. Central to the effectiveness of these practices was the notion of trust. Most of the doctoral students trusted their supervisor to engage in the four practices to support them. Through the provision of timely and thoughtful feedback and feedforward strategies from their community, doctoral students were able to develop their academic writing as a tool for communication, as a tool for thinking and creating new knowledge, and for developing their academic identities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".