MétaCan
Menu
Back to cohort
Record W2171046257 · doi:10.2304/elea.2014.11.2.154

Polysynchronous: Dialogic Construction of Time in Online Learning

2014· article· en· W2171046257 on OpenAlexaff
Murat Öztok, Lesley Wilton, Kyungmee Lee, Daniel Zingaro, Kim MacKinnon, Alexandra Makos, Krystle Phirangee, Clare Brett, Jim Hewitt

Bibliographic record

VenueE-Learning and Digital Media · 2014
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsDialogicAsynchronous communicationPerceptionTechnological determinismOnline learningDeterminismAsynchronous learningOnline discussionPsychologyComputer scienceSociologySynchronous learningMathematics educationPedagogyEpistemologyMultimediaWorld Wide WebTeaching methodCooperative learningSocial science

Abstract

fetched live from OpenAlex

Online learning has been conceptualized for decades as being delivered in one of two modes: synchronous or asynchronous. Technological determinism falls short in describing the role that the individuals' psychological, social and pedagogical factors play in their perception, experience and understanding of time online. This article explores the history of synchronous and asynchronous concepts and argues that an examination of students' perception of time in online contexts is required if we are to move past asynchronous-synchronous dualities toward a more nuanced understanding of how time manifests itself and affects pedagogical practices. Bakhtin's concept of the dialogic is used as a framework to explore how time in online learning has been reshaped through dialogue. A new description of time online as being polysynchronous is suggested and the illustrations provided explore the educational implications of this time shift on online discussions.

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)0.003
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.019
Scholarly communication0.0060.013
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueE-Learning and Digital MediaSame topicInnovative Teaching and Learning MethodsFrench-language works237,207