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Record W1981380484 · doi:10.1558/japl.v5i1.31

Is it Relevant? The Role of Off-task Talk in Collaborative Learning

2012· article· en· W1981380484 on OpenAlexaff
Khaled Barkaoui, Margaret So, Wataru Suzuki

Bibliographic record

VenueJournal of Applied Linguistics and Professional Practice · 2012
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsPerspective (graphical)Sociocultural evolutionTask (project management)PsychologyInsiderCollaborative learningSociocultural perspectiveCognitionPresentation (obstetrics)Action (physics)Social psychologyCognitive psychologyPedagogySociologyComputer scienceEpistemology

Abstract

fetched live from OpenAlex

We adopt a sociocultural approach to examine the characteristics and roles of the off-task talk (OTT) of three graduate students engaged in a collaborative learning task. Our focus is on three OTT episodes during three out-of-classroom meetings to discuss an academic article in preparation for a graded classroom presentation. In order to obtain an insider perspective on these episodes, each of the three students independently analyzed each OTT episode in terms of its characteristics, roles, and effects on the participants’ contributions and relationships. The results showed that the OTT episodes were short and infrequent; had an academic flavor to them; weaved smoothly on and off task; and played various social, affective, and cognitive roles in collaborative learning. We argue that definitions that emphasize the content, rather than the functions, of OTT are misleading and that a sociocultural theory perspective allows a more positive view of OTT as a socio-affective action within a larger cognitive activity. We close by outlining some of the limitations of the study and a call for further research on OTT in collaborative learning.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.440

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 integrity0.0000.001
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 head0.031
GPT teacher head0.424
Teacher spread0.393 · 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 designNot applicable
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

Citations6
Published2012
Admission routes1
Has abstractyes

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