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Record W2329471272 · doi:10.5539/hes.v6n2p61

A Multi-Perspective Investigation into Learners’ Interaction in Asynchronous Computer-Mediated Communication (CMC)

2016· article· en· W2329471272 on OpenAlexvenueno aff
Çiğdem Suzan Çardak

Bibliographic record

VenueHigher Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAsynchronous communicationComputer-mediated communicationPerspective (graphical)PsychologyGraduate studentsMathematics educationComputer sciencePedagogyWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

<p>This article focusses on graduate level students’ interactions during asynchronous CMC activities of an online course about the teaching profession in Turkey. The instructor of the course designed and facilitated a semester-long asynchronous CMC on forum discussions, and investigated the interaction of learners in multiple perspectives: learners’ views, participation in terms of quantity, participation in terms of discussed issues and collaborative construction of new knowledge. 14 graduate students were participated in the study and 12 of them were interviewed. Meanwhile, 345 messages sent by the learners and the instructor were analyzed in order to identify discussed issues and social construction of knowledge. The results of the study showed that according to the message numbers and views of the learners, learner-instructor interaction was ahead of learner-learner interaction. Meanwhile, learner-content interaction was sustained by various discussion topics. Though learners’ views related to learner-learner, learner-instructor and learner-content interactions were positive in general, analyzing the contents of the messages didn’t reveal higher levels of co-construction of knowledge according to the Interaction Analysis Model.</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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.069
GPT teacher head0.413
Teacher spread0.344 · 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 designQualitative
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

Citations3
Published2016
Admission routes1
Has abstractyes

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