Use of Facebook by Secondary School Students at Nuku'alofa as an Indicator of E-Readiness for E-Learning in the Kingdom of Tonga
Bibliographic record
Abstract
The Kingdom of Tonga is an isolated least developing country located on the northeast of New Zealand with a population of 103,252 (2011 census) and with a gross domestic product per capita of USD $2,545.20. Before educational systems in a least developing country like the Kingdom of Tonga begin employing e-learning, an assessment of the current situation of students and learning institutions may contribute to its success. Using an appropriate assessment tool is important for accurately measuring the degree of e-readiness. In this study, we administered a survey to 186 students randomly selected from five secondary schools in the Kingdom of Tonga to measure Facebook usage as an index of e-readiness for e-learning. We found that a large percentage (81%) of secondary students use Facebook, and most (74%) of these students have used Facebook for two or more years. All (100%) students use a computer to access Facebook, and most also access Facebook through mobile phones (62%) or tablets (46%). We also found correlations between duration of having a Facebook account and other indicators of e-readiness. Our findings suggest that secondary students in the Kingdom of Tonga have developed e-readiness for e-learning through their use of Facebook.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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 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".