The Differential Nature of Remote Learning Among University Students
Bibliographic record
Abstract
One of the many drastic effects of the COVID-19 pandemic in early 2020 was a sudden shift to remote learning for post-secondary students. This study aims to build a foundation for that understanding, with a particular focus on addressing the effects on students who were working concurrently with their studies through the pandemic. A survey was conducted, gathering 181 responses from undergraduate computing students attending Mount Royal University. The survey queries the students’ experience with work-school balance during the pandemic, their feelings about online classes, the perceived positive and negative aspects of learning online, and whether they would opt into online classes in the future in the absence of any pandemic-related concerns. The results show a clear perception of increased flexibility (88%) coupled with an increase in the students’ ability to manage their time (61%). Given that 74% of the respondents report that online classes are more convenient than in-person classes while only 22% report a negative impact on their performance, this study concludes that online learning opportunities may correlate with an easing of stress on post-secondary students without significantly impacting academic performance for certain personality types, while others report significantly negative experiences with respect to their mental health.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".