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Record W6893275020 · doi:10.5281/zenodo.15706309

MANAGEMENT OF EDUCATIONAL CHALLENGES OF E-LEARNING APPLICATIONS AT PUBLIC TERTIARY INSTITUTIONS DURING AND POST COVID-19 ERA

2025· article· en· W6893275020 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicE-Learning and COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationGovernment (linguistics)PandemicQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)Public institutionCompliance (psychology)

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.045
GPT teacher head0.317
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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