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Record W4281663779 · doi:10.5539/jel.v11n4p74

Socially Shared Regulation and Performance in Group Work on Creativity Tasks: Analyzing Regulation Utterances

2022· article· en· W4281663779 on OpenAlexvenueno aff
Takamichi Ito, Takatoyo Umemoto

Bibliographic record

VenueJournal of Education and Learning · 2022
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPsychologyCognitionCollaborative learningSocial psychologyCognitive psychologyUtteranceComputer-mediated communicationMetacognitionTask (project management)PedagogyLinguistics

Abstract

fetched live from OpenAlex

This study examined socially shared regulation of learning (SSRL) and motivation processes in a collaborative learning task that required creativity using the ICT tool of mind mapping. Thirty university students formed three groups, collaborating face-to-face to generate creative ideas. The following results were obtained from qualitative and quantitative data using psychological scales and utterance analysis. In the middle phase of the collaborative activity, there was a significant weak-to-moderate positive correlation between socially shared regulation of cognition, self-regulation, co-regulation, and socially shared regulation of intrinsic motivation and a deep level of regulation utterances. Moreover, there were significant weak-to-moderate correlations between behavioral and cognitive engagement, SSRL of monitoring and cognition, and the three modes of motivational regulation. Creative performance was significantly and moderately positively associated with socially shared regulation of cognition and total frequency of utterances in the group. Based on these findings, the implications for practice in university education are discussed from the perspective of socially shared regulation 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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.368
Teacher spread0.332 · 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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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