Student perceptions of post-pandemic university learning: challenges and benefits
Bibliographic record
Abstract
Purpose This study aims to investigate university students’ perceptions and experiences of postpandemic campus environments. Posts from a subreddit dedicated to a large, research-intensive Canadian university were analyzed to understand how students were affected by the evolving circumstances, their coping strategies and the resources that could support their flourishing in both academic and campus life. Design/methodology/approach A total of 42 Reddit posts from 427 unique users were selected for qualitative thematic analysis. Three researchers independently analyzed the posts and developed codes representing recurring themes, categories and subcategories. Discrepancies were resolved through discussion to establish the final themes. Findings Six major themes emerged from the analysis: modality of learning, perceived rights, attitudes and expectations, public health safety, political nature, mental health and well-being and communication patterns. Students expressed mixed preferences for learning modalities, concerns about accommodations and perceived freedoms, apprehension about contracting future viruses and the impact of changing university policies on their mental health and well-being. Originality/value This study contributes to the growing understanding of university students’ experiences post-COVID-19 pandemic, specifically focusing on their return to in-person learning and their evolving relationships with the campus community. The findings highlight the importance of addressing students’ concerns and providing appropriate resources to support their mental health and well-being during major transitional periods.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".