College faculty experiences with online teaching during the COVID-19 pandemic
Bibliographic record
Abstract
Objective: The COVID-19 pandemic led to a rapid transition in operations for higher education institutions. The delivery of traditional teaching methods shifted to online instruction. Much of the research has explored student experiences during COVID-19 pandemic. The purpose of this project was to explore faculty experiences with COVID-19 in a Midwest state within the United States.Methods: The team utilized a convenience sample of faculty employed at colleges and universities in a Midwest state that taught in the spring of 2020. An email was sent to the faculty, informing them about the nature and purpose of the study and the criteria for participation along with a link to the survey. The survey was a 33-item online survey utilizing Qualtrics®.Results: The sample size for this study was N = 329. Majority of respondents (n = 89) felt that the transition to online learning was difficult. Lastly, common challenges reported were communication with students and peers and isolation.Conclusions: Understanding faculty experiences during the COVID-19 pandemic is essential to future teaching curriculum. The majority of faculty felt that transition to online learning was difficult and affected their mental wellbeing. Communication with students was identified as the biggest challenge for faculty. Yet, faculty felt as though student grades were not affected. As future curriculum and faculty experiences are explored, an emphasis on improving student-faculty communication must be priority. Exploration regarding mental wellness services and resources should be considered for faculty within higher education institutions.
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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 | 0.005 | 0.015 |
| 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 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".