Studying and Learning Psychology During the COVID-19 Pandemic: A Mixed-Methods Approach on Students’ Perspectives of Psychological Well-being and Adjustment to Studying Online
Bibliographic record
Abstract
Background: The challenges presented by coronavirus disease 2019 (COVID-19) in higher education pressured learners and instructors to incorporate online emergent learning which presented several well-being and academic challenges to students. Objective: The purpose of this study is to examine the impact of studying online to students’ well-being. Methods: A mixed methods approach was followed for this study. Eighty students completed an online survey that measured their stress level of studying online, and 13 semistructured interviews were conducted at Queen's University Belfast. Results: Findings suggest that online learning under such circumstances increased students’ level of stress due to a number of perceived factors. Our findings also reveal the journey of student adjustment to online learning, reflecting the flexibility of blended learning as a long-term pedagogical strategy in universities, necessary for University's survival. Conclusion: As demonstrated in this study, after the initial difficulties of moving to online learning which had negative impacts on students learning and well-being, students subsequently adjusted to the online learning environment documenting students’ adaptability to a new learning environment and highlighting student resilience.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".