Incorporating Open Educational Practices in Graduate Education: A Collaborative Autoethnographic Study
Bibliographic record
Abstract
In this paper we describe the early steps of our journey through a collaborative autoethnographic research project and share preliminary findings. As distance educators who work at an open, online university, we embrace a philosophy of openness, drawing on open educational practices to facilitate collaborative and flexible learning. As faculty members who support masters and doctoral students, we conceptualize our virtual learning environments as spaces where reciprocal learning takes place between and among learners and professors in a form of co-mentorship. We chose collaborative autoethnography because it is an approach that allows us to interrogate our practice using experiences, archival data, and artifacts as accessible and reliable sources of information. Collaborative autoethnography, which permits us to both individually and collectively critique our practice, requires us to consider our personal experiences in relation to our identities as distance educators within the cultural context of an open and online research university in Canada. The initial data analysis process has uncovered three emergent themes to date. These themes include values linking open educational practices with student engagement and facilitating effective open educational practice through learning design. This research project enables us to experience the power of collaborative autoethnography as a research approach and to further our understanding of the potential of open educational practices in graduate education.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".