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Record W1574943698 · doi:10.37119/ojs2010.v16i1.48

Educators’ Perceptions of Uses, Constraints, and Successful Practices of Backchanneling

2013· article· en· W1574943698 on OpenAlexvenueaboutno aff
Cheri Toledo, Sharon Peters

Bibliographic record

Venuein education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)PerceptionPsychologyQualitative researchProfessional developmentPedagogyComputer scienceSociology

Abstract

fetched live from OpenAlex

This qualitative study sought to explore participants’ perceptions of the impact of web-based backchanneling conversations in a variety of learning environments. Backchannels, forms of instant message conversations, take place during synchronous learning sessions. Online interviews with educators from Canada and the United States revealed their perceptions of the uses, constraints, and successful practices of backchanneling. Educators in the study saw backchanneling as a non-disruptive, non-subversive, collaborative activity that expanded participation and interactions; an approach applied with intentionality to enhance learning. Six themes emerged from the data: backchanneling for professional development and networking; backchanneling for engagement; constraints of backchanneling; changes in teacher and/or learner perspectives; examples of backchanneling in educational settings; and suggestions for successful backchanneling.Keywords: web-based backchanneling; learning environments; professional development; networking

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)0.013
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.374
Teacher spread0.352 · 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 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

Citations13
Published2013
Admission routes2
Has abstractyes

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