Evolution of the Perceived Impact of the Pandemic on University Students
Bibliographic record
Abstract
The Coronavirus pandemic has taken the world hostage. All aspects of society have been affected, including the education system with the closure of universities and the adoption of abrupt measures to continue offering university programs virtually. Unexpectedly, the difficult situation has continued until at least December 2021. This paper studies the evolution of the perceived impact of the pandemic on students over four semesters, from Winter 2020 to Fall 2021. A survey conducted at the end of each semester captured the evolution of the impact felt by students. Using Text Mining and Sentiment Analysis, per semester, per gender and per age category, the progression of certain sentiments was identified. The study reveals that the professor’s attitude and support was a key element at the beginning of the pandemic and for many, it has been a good learning experience overall. The loss of direct/in person communication has been strongly felt and it got worse as time progresses. The level of negative comments seems to decrease over time for Female students, while for Male students, it tends to increase. Students from different age groups also reacted differently. Students in the most prevalent age group from age 25 to 30 show at first a decline in the proportion of negative comments followed by an increase, while older students from the 30 to 35 age group have a steady decrease of negativity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".