Faculty and Student Online Experiences Amidst the COVID-19 Pandemic: A Descriptive Study (Part 1)
Bibliographic record
Abstract
Background: With the declaration of a global pandemic in March 2020, post-secondary institutions closed campuses, learner clinical experiences were suspended, and teaching moved to a fully online format. Prior to data collection, a literature review yielded few results beyond editorials, student and faculty reflections, and blog postings in relation to COVID-19. It is crucial that we learn from the experience of students and faculty to evaluate the novel teaching and learning realized during the pandemic and prioritize a scholarly plan including potential innovative approaches for future educational programming. Objectives: The overall goal for this multi-site research was to capture the perspectives of nursing students, and nursing faculty members on their teaching and learning experiences online during a declared pandemic and provincial state of emergency. Design: Descriptive survey study. Setting: Online environment in one province in Eastern Canada. Participants: Nursing students and faculty in three Canadian bachelor of science in nursing programs during the spring and summer semester 2020. Method: Participants were invited via email to complete an online survey (via Opinio) related to their experience of learning or teaching in the fully online environment. Results: Quantitative data were analyzed using descriptive (frequencies, means, modes) statistics to describe the experience from the participants’ perspectives and inferential (Chi-square test, t-test) statistics to investigate perceptual differences between the faculty members’ and the students’ perspective related to the effectiveness, engagement, and comfort in the online learning and teaching experience. Qualitative data were analyzed using thematic analysis. The focus of this article is the presentation and discussion of the quantitative data. Conclusions: The resulting knowledge provides an in-depth understanding of the fully online learning and teaching experience during a global pandemic that is invaluable to inform future program planning in relation to online learning and teaching in a practice profession.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".