Social Media Application in Education During the COVID-19 Pandemic; Pros and Cons: A Systematic Review
Bibliographic record
Abstract
Background: In addition to morbidity and mortality, the COVID-19 pandemic affected various fields such as medical and academic education. The purpose of this study was to investigate the types of social media used in medical and academic education and identify their advantages and disadvantages. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted to identify published studies related to the effects of social media on medical and academic education during the COVID-19 pandemic. The retrieved records were screened in a two-step process; first by title/abstract and then by full text by two independent researchers and the most relevant studies were selected applying the eligibility criteria. Results: Facebook, YouTube, Zoom, WhatsApp, Moodle, and Skype were the most used platforms. The main purpose of using these applications was to provide distance education to students in the pandemic era. The advantages of using online platforms outweighed the disadvantages. Advantages include the availability of information at any time and place, maintaining communication between students and classmates and instructors, and the possibility of presenting conferences and assignments. Disadvantages comprised infrastructure and internet problems. Conclusion: Social media and messengers have a great potential to meet educational purposes in the epidemic era. Although online platforms can serve as an efficient public repository of learning resources, achieving this needs some prerequisite and infrastructural tasks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.239 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".