The Academic and Social Impact of COVID-19 Among College Students: Perspectives from the United States of America, Cameroon, Ghana, and Nigeria
Bibliographic record
Abstract
The novel coronavirus disease of 2019 (COVID-19) caused disruptions in the delivery of higher education around the globe. To understand how universities and students are dealing with the sudden change from in-person course delivery to online format, this cross-sectional mixed-method study aimed to (a) ascertain the impact of the COVID-19 pandemic on students’ ability to access online learning; (b) examine how college students adapted to changes in the learning/teaching environment; and (c) explore the students’ perspective on measures that institutions of higher learning could have adopted to ease the abrupt transition to online learning. Results indicate a majority of participants in the US reported access to internet and computers for off-campus learning during the COVID-19 pandemic. A little over half of participants from Africa reported internet access during the COVID-19 pandemic (82% of participants from Nigeria and 66.7% from Ghana). Participants from Cameroon reported the lowest percentage of access to online learning at 59.1%. Participants from Africa reported challenges in adapting to online format due to inadequate access to necessary technological resources such as a reliable internet and computer. Participants identified internal and external resources that could have been adopted to better deal with the transition to online learning. Institutions of higher learning can learn from their initial response to the COVID-19 pandemic to formulate and adjust policies that provide flexibility to effectively transition to online learning while catering to the social, educational and health needs of their students.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".