MANAGEMENT OF EDUCATIONAL CHALLENGES OF E-LEARNING APPLICATIONS AT PUBLIC TERTIARY INSTITUTIONS DURING AND POST COVID-19 ERA
Bibliographic record
Abstract
COVID-19 has wreaked havoc on the majority of the world’s economies. In most nations throughout the world, education is the only industry that has totally transmitted to online form. During the pandemic online learning was the best option for continuing education, particularly in post-secondary education. The first quarter of 2020 was a difficult time for the global community. The Coronavirus (COVID-19) pandemic that swept the world affected many aspects of human endeavour, from the decline in industrial production to the readjustment of the academic calendars of all educational institutions worldwide. Efforts to reform education as a result of the prolonged lockdown compelled the government to impose e-learning in tertiary institutions across the country. It is important to note, however, that these directions did not result in significant change due to inadequate infrastructure and network management. As a result, this study evaluated compliance with e-learning during the COVID-19 pandemic shutdown in Nigeria’s tertiary institutions in relation to education factors and constrains faced. Through an online Google form, a systematic selection approach was used to choose 388 respondents from various institutions across Nigeria. This study discovered the educational variables are significantly related to e-learning compliance, with academic attainment serving as the major predictor. It was also discovered that there was variation in e-learning compliance across the selected public tertiary institutions, indicating that e-learning has been effectively incorporated into tertiary education in Nigeria, public universities which had forced long break, has the lowest of e-learning compliance during the COVID-19 pandemic, which can be attributed to lack of connectivity. Data limit, poor data speed, little/no face to face interaction, intense requirement for self-discipline, lack of a multiplier of device, poor learning. The limitations impede compliance with e-learning, which would have a multiplier effect on academic progress at the institutions and might and might further widen the nation’s socio-economic skills gap, both on management and academic provisions. The study’s findings will be very useful to university administrators and management in making future emergency choices on the deployment on online learning programs for students from various backgrounds
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".