Exploring our Open Educational Practices in Support of Excellence in Graduate Education: A Collaborative Autoethnography
Bibliographic record
Abstract
This paper presents the findings of our exploration of our open educational practices (OEP) with graduate students. As reflective practitioners, we used a self-study methodology and collaborative autoethnographic methods to interrogate our open approaches to teaching and supervision. We draw on our developing competencies to support the wisdom, critical thinking, resilience, and adaptability of our graduate students. The article extends preliminary findings about graduate education and open practices in relation to and emerging from earlier work (Ives et al., 2022), which committed us to further exploration of our practices. Using our definition of OEP which expands on the work of several open scholars, we report new findings from our in-depth collaborative analysis of data collected over two years. We found a gap in the literature examining the use of OEP with graduate students. Findings include OEP and their alignment with our values and competencies, as well as OEP within our teaching, course design, and graduate supervisory practices. We offer insights into the outcomes of our practices for students and ourselves, and ways we can improve.
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".