Parallel Teaching Processes to Mitigate Learning Disruption in the Pandemic
Bibliographic record
Abstract
ABSTRACT The 2020 COVID-19 worldwide pandemic has interrupted all lives in some form. For post-secondary students, moving to various form of online learning has caused additional stresses that complicate learning. The overall climate of uncertainty, fear, grief, groundlessness, and disconnection are almost ‘ethereal’ life themes that are, on the one hand, difficult to articulate, and on the other, keenly felt. Educators are inundated with training opportunities to transition to remote, hybrid, and online delivery. As teachers experience the same disruption as their students, they are in a unique and privileged position to thoughtfully engage students in a teaching-learning dynamic that models Transformative Learning principles. This essay explores four practices to connect the shared disruptions shared by students and teachers alike, while articulating parallel methods for teachers to support students. Concepts of patience, flexibility, limit-setting, and equanimity are explored as ways to enhance teaching during this pandemic. While the ‘ethereal’ pervades teaching and learning, the ideas proposed in this essay will help bridge the gap for students and teacher to experience an education that promotes transformation and ownership of learning.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".