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Record W2219581871 · doi:10.5539/jel.v5n1p87

The Perceptions of Participation in a Mobile Collaborative Learning among Pre-Service Teachers

2015· article· en· W2219581871 on OpenAlexvenueno aff
Shih-Hsiung Liu

Bibliographic record

VenueJournal of Education and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsCollaborative learningPerceptionPsychologyScheduleService-learningSense of communityService (business)Mathematics educationPedagogyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

<p>This study uses Facebook as a platform and arranges certain learning tasks to identify the feasibility of mobile collaborative learning for pre-service teachers. The pre-service teachers’ sense of community and perceptions of collaborative learning are investigated. A total of 153 pre-service teachers volunteered to participate in an Intern Mobile Collaborative Learning Facebook Group from July 2015 to October 2015. During participating in the Facebook Group, pre-service teachers were required to achieve various tasks regarding collaborative learning. A questionnaire, consisting of three sections, frequency, sense of community, and perceptions of learning and perceptions of collaborative learning, was developed and validated. All participants were required to fill in the questionnaire at the last week of the project’s schedule. This study concludes that high browsing frequency on Facebook Group could positively facilitate the sense of community and perceptions of collaborative learning among pre-service teachers; while high frequency of posting and responding to messages on Facebook Group merely promotes perceptions of collaborative learning. The conclusion identifies that assigned tasks like posting and responding to messages regarding school field-based experiences are necessary in mobile collaborative learning among pre-service teachers.</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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.385
Teacher spread0.366 · 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 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

Citations16
Published2015
Admission routes1
Has abstractyes

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