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Record W1847817774 · doi:10.19173/irrodl.v4i2.148

Computer-Mediated Communication: A vehicle for learning

2003· article· en· W1847817774 on OpenAlexvenueno aff
Linda D. Grooms

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsImmediacyDistance educationComputer-mediated communicationPsychologyInteractivityExperiential learningEducational technologyPedagogyComputer scienceMultimediaMathematics educationThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

The axiom of humanity’s basic need to communicate provides the impetus to explore the nature and quality of computer-mediated communication as a vehicle for learning in higher education. This exploratory study examined the experiential communication perceptions of online doctoral students during the infancy of their program. Eighty-five students were electronically queried through a 32 item open-ended questionnaire within a 13 day time frame. Preliminary findings supported the experience of Seagren and Watwood (1996) at the Lincoln Campus of the University of Nebraska, that “more information widens learning opportunities, but without interaction, learning is not enhanced” (p. 514). The overarching implications stress that faculty development and instructional planning are essential for the effective delivery of online courses, and even more so when collaborative learning is used. Facilitating group communication and interaction are areas beckoning attention as we continue to effectively organize the online classroom of this new millennium. Key Words: Computer-mediated communication, online instructional pedagogy, virtual classroom, online learning, higher education, interaction, immediacy

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0090.009
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.105
GPT teacher head0.478
Teacher spread0.373 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2003
Admission routes1
Has abstractyes

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