Demographic Variables and Students Use of E-learning Resources in Private Secondary Schools Libraries in Rivers State of Nigeria
Bibliographic record
Abstract
The applications of e-learning resources to studies, teaching and learning by both staff and students have been investigated. However, the provision of e-learning tools for stake-holders is a modern goal to improving as well as achieving the quality of educational system in the twenty first century. Students’ demographic variables and the use of e-learning resources in selected private secondary schools in Rivers State as presented in this research has detailed expository facts. The impacts of age, gender, class level availability, accessibility, and human resources as independent variables in the use of e-learning resources were determined. Six research questions and hypotheses each were validated or invalidated depending upon the respondents results. Linear regression, t-test and multiple regression analyses were employed as statistical tools. It was discovered that there are immense influences of some demographic independent variables such as age, gender, availability, accessibility, and human resources in the use of e-learning resources in private secondary schools in Rivers State. Besides availability and accessibility were major contributors to the associated relationships in the use of e-learning resources. Equally needful are human resources and students' skills. There was contrast distinction between class level and other independent variables since it had no significant effect the uses of e-learning resources within the domain for Private schools in Rivers State
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".