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Record W2289893482 · doi:10.19173/irrodl.v17i2.2107

Dyads Versus Groups: Using Different Social Structures in Peer Review to Enhance Online Collaborative Learning Processes

2016· article· en· W2289893482 on OpenAlexvenueno aff
Francesca Pozzi, Andrea Ceregini, Lucia Ferlino, Donatella Persico

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentSocial constructivismPsychologyInformal learningFacilitationPoint (geometry)Class (philosophy)Collaborative learningQuality (philosophy)Qualitative researchMathematics educationSocial psychologyComputer sciencePedagogySociology

Abstract

fetched live from OpenAlex

<p>The Peer Review (PR) is a very popular technique to support socio-constructivist and connectivist learning processes, online or face-to-face, at all educational levels, in both formal and informal contexts. The idea behind this technique is that sharing views and opinions with others by discussing with peers and receiving and providing formative feedback enriches the quality of learning. In this study, a class of trainee teachers conducts an online PR. The resulting interactions are analyzed and evaluated by the researchers through the application of an evaluation model based on both quantitative and qualitative data. In particular, two conditions are studied, namely the PR in groups versus the PR in dyads. Results show that students who carried out the PR in groups were less active from the cognitive point of view, while they devoted more effort to deal with organizational matters and discourse facilitation.</p>

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)0.012
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.216
GPT teacher head0.587
Teacher spread0.371 · 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.

Study designOther design
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

Citations7
Published2016
Admission routes1
Has abstractyes

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