MétaCan
Menu
Back to cohort
Record W2471811843 · doi:10.19173/irrodl.v17i4.2333

Use of Facebook by Secondary School Students at Nuku'alofa as an Indicator of E-Readiness for E-Learning in the Kingdom of Tonga

2016· article· en· W2471811843 on OpenAlexvenueno aff
Hans Tobias Sopu, Yoshifumi Chisaki, Tsuyoshi Usagawa

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaPsychologyGross domestic productPopulationCensusKingdomMedical educationMathematics educationGeographyDemographySociologyMedicineEconomic growth

Abstract

fetched live from OpenAlex

<p class="2">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.</p>

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)0.011
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.115
GPT teacher head0.492
Teacher spread0.377 · 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 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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe International Review of Research in Open and Distributed LearningSame topicImpact of Technology on AdolescentsFrench-language works237,207