Secondary Students’ Educational Experiences During the COVID-19 Pandemic: A Qualitative Evidence Synthesis
Bibliographic record
Abstract
Recognizing the urgent need to understand how education systems can effectively respond to global crises, we conducted a qualitative evidence synthesis to examine the educational experiences and psychosocial wellbeing of secondary students during the COVID-19 pandemic (March 2020–January 2024). Comprehensive searches were conducted across eight electronic databases, resulting in 41 eligible studies. Thematic synthesis revealed five descriptive themes: challenging online learning experiences; benefits of online learning; complexities associated with education-related disruptions and transitions; social connections and support; and emerging educational needs. Twenty corresponding subthemes were also identified. Lastly, three analytical themes were developed based on the literature reviewed, including student resilience and adaptability through crisis; the digital divide and educational inequality; and reimagining the future of education. Findings revealed that secondary students experienced several education-related challenges and benefits during the pandemic; they also highlight the need for effective, inclusive, and accessible educational practices that can be adopted now and in future crises. This review represents an important and timely contribution to the literature via its explicit focus on secondary students worldwide and the application of a novel and rigorous qualitative synthesis methodology, with implications for the evolving educational landscape.
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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 | 0.030 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".