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Record W2779476696 · doi:10.5430/wje.v7n6p90

Undergraduates’ Attitude Towards the Use of Social Media for Learning Purposes

2017· article· en· W2779476696 on OpenAlexvenueno aff
Cheta Williams, Rebecca Yinka Adesope

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPort harcourtPsychologySocial mediaTest (biology)Scheffé's methodReliability (semiconductor)Null hypothesisMathematics educationPopulationSample (material)Applied psychologySocial psychologyMedical educationStatisticsAnalysis of varianceComputer scienceMathematics

Abstract

fetched live from OpenAlex

The study investigated Undergraduates’ attitude towards the use of social media for learning purposes. It wasconducted at the University of Port Harcourt, Rivers State, Nigeria. Two objectives and two null hypotheses wasused to investigate the study. The population used were Undergraduate students from three faculties at the Universityof Port Harcourt. A sample of 300 students were randomly selected from three Faculties. Simple random stratifiedsampling techniques was used for the study and the instrument used to collect data was a structured questionnaireentitled Undergraduates’ attitude towards the use of social media for learning purposes (UATUSMLP) with 42 items.Mean score, ANOVA, Z-test and Scheffe’s model were the statistical tools for the study. The Instrument was givento experts in the field of educational technology to ensure its validity. Test retest was applied to ensure reliability ofthe instrument and a reliability coefficient of 0.84 was obtained. It was found that social media are used foreducational purposes in terms of quick growth in knowledge and information. It addition it was found thatundergraduates bound with close and prospective groups for change. It is recommended that Universities should beacquainted with students want and concern in the schools.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.177
GPT teacher head0.405
Teacher spread0.228 · 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 designNot applicable
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

Citations16
Published2017
Admission routes1
Has abstractyes

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