The Design and Evaluation of Online Faculty Development for Effective Graduate Supervision
Bibliographic record
Abstract
This design-based research aims to improve the quality of graduate supervision using a Massive Open Online Course (MOOC). The Quality Graduate Supervision MOOC brings interdisciplinary faculty, postdoctoral scholars, and expert supervisors together in an online learning community to discuss and consider effective supervision practice, strategies for relationship building, supports for academic writing, mentoring for diverse careers, and how to combine excellence and wellness. The survey, interview, and system data were analyzed to inform and assess the design and development of the QGS MOOC, to gain insights into learner experience and engagement, and to assess the impact of the online learning community on graduate supervision practices. Through ongoing design and evaluation of this online learning course for graduate supervisors, the research team found the learning community influenced faculty members’ awareness, collective knowledge building, goal setting, and actions for graduate supervision practice. We present results from our evaluation of the design components in the QGS MOOC, the learning benefits for supervisors, impacts on graduate supervision practice, and make several recommendations for research and practice.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".