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Record W1596307662 · doi:10.21432/t25g6n

Analyse de la nature constructiviste d'une activité d'apprentissage collaboratif médié par les TIC

2003· article· en· W1596307662 on OpenAlexvenueno aff
Nancy Brousseau, Jesùs Vàzquèz-Abad

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2003
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsConstructivism (international relations)Constructivist teaching methodsPedagogyMathematics educationMetacognitionCooperative learningCollaborative learningActive learning (machine learning)PsychologySociologyTeaching methodEpistemologyComputer sciencePhilosophyCognitionArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

Abstract. In this article we examine an example of collaborative learning with ICT in secondary school science, in order to analyse its characteristics and determine if they answer criteria for an activity of constructivist nature. We retain that constructivism places the student at the centre of her learning; opposes the notion of learning as transmission of knowledge; requires an active participation of the learner who must build and reconstruct, individually and collectively, her knowledge in order to solve a problem or realise a project. In light of these criteria, we conclude that an activity such as the TACTICS project one, can be considered of a constructivist nature.

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 categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.010
GPT teacher head0.314
Teacher spread0.305 · 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 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

Citations4
Published2003
Admission routes1
Has abstractyes

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