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Record W2780092491 · doi:10.5430/wje.v7n6p75

Awareness and Usage of E-Learning Materials among Students of National Open University of Nigeria (NOUN)

2017· article· en· W2780092491 on OpenAlexvenueno aff
Nwana S.E., Cajetan Ikechukwu Egbe, Sylvanus Ochetachukwu Ugwuda

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Politics and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionNounPsychologyOpen universityDescriptive statisticsMathematics educationDistance educationPopulationDescriptive researchMedical educationComputer scienceMathematicsStatisticsArtificial intelligenceMedicineEnvironmental health

Abstract

fetched live from OpenAlex

The study focused on awareness and usage of e-learning materials among students in the distance educationprogramme of the National Open University of Nigeria (NOUN). The study is a descriptive survey which was guidedby two research questions. The population for the study comprised of the 1, 512 year three students out of which 400were sampled and used for the study. The instrument for data collection was a 30-item self-constructed checklisttitled, “Distance Education Students’ Awareness and Usage of E-Learning Materials” (DESAUELM). It wasvalidated by experts and the reliability co-efficient stood at 0.86. The data collected were analysed using frequenciesand percentages. The findings showed that, the students are aware of majority of the e-learning materials. Also, theresult on usage indicated that the students do not use majority of the e-learning materials. There was general lowusage of the e-learning materials as revealed by the findings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.039
GPT teacher head0.297
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2017
Admission routes1
Has abstractyes

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