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Record W1972320485 · doi:10.5539/ies.v5n5p218

Potentials of E-learning as a Study Tool in Business Education in Nigerian Schools

2012· article· en· W1972320485 on OpenAlexvenueno aff
Ibhade Joy Ojeaga, V. I. Igbinedion

Bibliographic record

VenueInternational Education Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsEducational technologyIgnoranceOpen learningInformation and Communications TechnologyTeaching methodInformation technologyActive learning (machine learning)Mathematics educationBusiness educationHigher educationPsychologyPedagogySociologyCooperative learningComputer sciencePolitical science

Abstract

fetched live from OpenAlex

With advancement in information technology in the 21st century, e-learning has become an invaluable technology for teaching, learning and research in education. E-learning involves the use of technology to enhance learning including digital collaboration, satellite broadcasting, CD-ROMS amongst others. E-learning has so many advantages over the traditional method of teaching and learning. Besides, so many e-learning tools are available for teaching and learning. However, despite these numerous advantages, a careful look at Nigerian educational system shows that the use of e-learning is still rather slow or imaginary. One of the reasons for this may be due to ignorance, negative perceptions amongst students and teachers and; non-availability of e-learning facilities in the system. This paper critically examined the potentials and immense benefits of e-learning in education generally and business education in particular. The paper also looked into the Nigeria policy on information and communication technology (ICT) as it relates to education. It further presented various methods that may be employed in delivery business education lessons via e-learning. Recommendations were made that there should be awareness creation about the potentials and prospects of e-learning in Nigerian schools amongst others.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.448
Teacher spread0.409 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2012
Admission routes1
Has abstractyes

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