Challenges Faced by Students During the Covid-19 Lockdown: Rethinking the Governance of Higher Education in Cameroon
Bibliographic record
Abstract
Change which is inevitable due to the changing needs of individuals and the society usually comes with some challenges. Knowledge and understanding of these challenges are relevant for effective implementation and efficient outcome. This study examined the extent to which challenges faced by students during the lockdown period will inform policy makers on restructuring the governance of higher education in Cameroon. The study adopted a cross-sectional survey research design of quantitative approach. Questionnaire was used to collect data from 1029 postgraduate students. The statistical package for social science (SPSS) version 23.0, frequency counts and percentage were used to analyse the closed-ended questions while the thematic approach was used to analyse the open-ended questions. The Spearman’s rho test which is a non-parametric test was used to test the hypothesis. Results revealed that lockdown period significantly affected students’ learning in higher education institutions in Cameroon and this effect was very strong justified with an R-square value of 0.826, P = 0.000, far < 0.05 and a high Chi-Square value of 964.612 at a degree of freedom of 81. This effect is related to the challenges faced with respect to knowledge and skills in online learning, access to online resources and management of online studies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".