“I Did Not Sign Up For This”: Student Experiences of the Rapid Shift from In-person to Emergency Virtual Remote Learning During the COVID Pandemic
Bibliographic record
Abstract
Abstract Objectives: The main objective of this study was to explore students’ experiences of the emergency virtual remote teaching, which was implemented as a result of the COVID-19 pandemic. Method: 439 students enrolled at a community college in Canada responded to a survey that had Likert-scale and open-ended questions. Anderson’s model for online learning was used as an analytic lens to gain insight on student experiences. Descriptive statistics were used to make meaning of the data. Thematic analysis was done on student responses to open-ended questions. Results: Findings were organized according to Anderson’s six factors in online teaching, namely: (a) Independent Study; (b) Peer, Family, & Professional Support; (c) Structured Learning Resources; (d) Community of Inquiry; (e) Communication; and (f) Paced, Collaborative Learning. The study revealed both challenges and opportunities that students experienced during their transition to emergency virtual remote learning. Conclusions: The invitation to students to share what worked—and what didn’t—yielded a wealth of specific suggestions for engaging students, promoting accountability, and supporting collaborative learning. Implication for Practice: This study looked past anticipated pressure points to reveal critical teaching factors that challenge—or enable—students as they transition to emergency virtual remote teaching. Post-secondary instructors would be well served to consider how they promote self-efficacy, provide access to supports, fashion an online learning environment, develop community, communicate expectations, and encourage collaboration.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".