My COVID teacher – pedagogy and technology: Frontiers of online teaching in the creative writing classroom
Bibliographic record
Abstract
This article is an international collaboration of three creative writing tutors detailing our responses and practices in shifting from in-class to online instruction due to the COVID-19 pandemic. We are three scholars working at three different institutions (University of British Columbia, RMIT University, and the University of Technology Sydney) across two countries (Canada and Australia). We present collective autoethnographic responses and offer a menu of pedagogical practices for designing courses and teaching creative writing online. While one tutor had sound pedagogical practice in blended teaching, making the transition to online course delivery seamless, two of the tutors had little experience with online teaching and course design so their shift to online teaching was seismic, which led to unexpected creative solutions. Our insights are reflected in our narratives and the personal experiences that we bring into this article. The result, as discussed in this article, demonstrates how sound creative writing pedagogy can be designed for the digital classroom, and perhaps offer a post-pandemic glimpse into the future of creative writing pedagogy. Our article is largely anecdotal – neither comprehensive nor does it collate data beyond our small pool – our aim is to contribute and continue a conversation that has supported us during the pandemic. We have written to one another, with one another, and against one another, trying to articulate how we teach creative writing now, in an effort that others can continue the discussion of what they think and feel, will happen – what is happening – to creative writing teaching at other universities, states and countries, in the transition (temporary and permanent) to considering online teaching and learning as a vital part of our pedagogy going forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".