U.S. Faculty and Administrators’ Experiences and Approaches in the Early Weeks of the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has had a profound and rapid impact on higher education institutions across the world. In this study, we report the findings of a survey investigating the rapid transition to emergency remote teaching in the early weeks of the pandemic at public and private post-secondary institutions in the United States. Participants consisted of 897 faculty and administrators at 672 U.S. institutions. Findings reveal that with few exceptions nearly all reporting institutions transitioned to emergency teaching and learning approaches. Administrators reported that faculty with and without online teaching experience pivoted to online teaching, and nearly all administrators indicated that those who did not have online teaching experience were in the process of learning how to teach online. Regardless of whether faculty had previous experience teaching online or not, many faculty reported that they were using new teaching methods. A majority of faculty reported making changes to their assignments or exams as a result of transitioning to a new mode of delivery. Nearly half reported lowering the expected volume of work for students (including dropping assignments or exams) and/or shifting to a pass/fail model for this semester. The primary areas where faculty and administrators identified a need for assistance related to student support, greater access to online digital materials, and guidance for working from home. This study provides an early snapshot of efforts towards teaching and learning continuity at a large scale and provides some insights for future research and practice.
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 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".