Digital Practices & Applications in a Covid-19 Culture
Bibliographic record
Abstract
This article addresses reflections of one University instructor’s teaching and her pre-teacher education students’ innovative digital learning practices during the Covid-19 pandemic in Spring 2020. The question of How has one instructor embedded digital practices in her virtual teaching to engage and purposefully introduce and connect pre-teacher education students with diverse technologies and multimodalities of learning during a mandatory virtual instruction time? will be addressed and discussed. Student-centered practices such as group work, pair work, the use of Zoom breakout rooms, and multimodal literary responses through technology applications such as Flipgrid and Google Docs will be described and reflected upon. The instructor’s own teaching practices that have included weekly mentoring meetings with her education students and continuing individual coffee meetings in diverse settings will be highlighted as ways of demonstrating care and encouragement toward face-to-face students who have been transitioned as online students. The reflections outlined in this abstract draw upon the notion of technologies as providers of active interactions and will include snapshots of an instructors’ students’ digital artifacts such as Flipgrid, video-recorded monologues, and Google Doc news stories with students reflecting on the uses of multimodal technologies in their own future teaching practices. This manuscript will also include student reflections and a sidebar of suggestions for using Zoom with virtual teaching.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.030 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".