Reshaping the educational landscape: During and after the COVID 19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this paper is to describe and analyze the response to COVID-19 and evolution through different models of online instruction during the pandemic at a large Canadian university. This paper primarily focuses on the approach taken by the Faculty of Education including the necessary restructuring of the processes, organization of the workforce, support configurations, and institutional constraints. The factors that impacted changes in the curriculum are examined. Three distinct phases were identified and compared: 1) remote teaching, 2) fully online using a combination of synchronous and asynchronous instruction, and 3) a diversity of hybrid approaches. The paper highlights a number of challenges experienced with online education during the pandemic. Each one of them presents both barriers and opportunities. The process has made way for a potential transformation of educational practice at North American universities. This will likely come as a combination of increased knowledge and practice of online learning during the pandemic, and as a need to reshape traditional institutional structures to reflect the shifted landscape of education. It has opened discussions on equity and accessibility, learner-centered design, and the potential for change in the classroom and educational programming.
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.
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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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 it