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Record W2400807290 · doi:10.24059/olj.v8i2.1825

A CONSTRUCTIVIST METHOD FOR THE ANALYSIS OF NETWORKED COGNITIVE COMMUNICATION AND THE ASSESSMENT OF COLLABORATIVE LEARNING AND KNOWLEDGE-BUILDING

2019· article· en· W2400807290 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOnline Learning · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversidade de São PauloSimon Fraser UniversityUniversité Laval
KeywordsConstructivist teaching methodsComputer scienceCognitionKnowledge managementAsynchronous communicationNetworked learningConceptual changeSocial constructivismCollaborative learningSentenceKnowledge buildingCognitive sciencePsychologyMathematics educationArtificial intelligencePedagogyEducational technologyTeaching method

Abstract

fetched live from OpenAlex

This article presents a discourse analysis method designed to study networked cognitive communication processes in knowledge communities, such as conceptual change, higher order learning and knowledge building. The method is grounded on genetic epistemology and integrates constructivist and socioconstructivist theoretical concepts. The sentence (understood as judgment) is chosen as the unit of analysis, and the application of the method is further explained. In addition, a study of transcripts in an asynchronous networked community of nurses illustrates the method and demonstrates how conceptual change, collaborative learning and knowledge building can be identified. Advantages and limitations of the method are also 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.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.507
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.464
Teacher spread0.442 · 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