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Record W2050969803 · doi:10.1177/1046496404263765

Group-to-Individual Transfer of Learning

2004· article· en· W2050969803 on OpenAlexaff
Fernando Olivera, Susan G. Straus

Bibliographic record

VenueSmall Group Research · 2004
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyTransfer of learningGroup (periodic table)Social learningCooperative learningCognitionStructuringTask (project management)Group learningSocial psychologyCognitive psychologyTransfer of trainingTransfer (computing)Developmental psychologyMathematics educationTeaching methodPedagogyComputer science

Abstract

fetched live from OpenAlex

We investigate the effects of group collaboration on member learning in a laboratory experiment. We test the hypothesis, based on theoretical ideas from research on cooperative learning, that groups provide opportunities for transfer of learning to individuals and that such learning occurs via cognitive and social processes that arise during group interaction. Eighty-six students solved puzzles either individually, in groups, or individually while observing a group. Analysis of subsequent individual performance on a transfer task showed that participating in or observing a group caused transfer of learning, whereas working alone did not. Furthermore, results suggest that transfer of learning occurred mainly due to cognitive, but not social, factors. Implications for structuring group work are discussed.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.122
GPT teacher head0.397
Teacher spread0.275 · 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

Citations96
Published2004
Admission routes1
Has abstractyes

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