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Record W2013151400 · doi:10.4018/ijopcd.2013010101

Cooperation and Collaboration in Higher Education

2013· article· en· W2013151400 on OpenAlexaff
Milton Campos, Lia Beatriz de Lucca Freitas, Cristina Grabovschi

Bibliographic record

VenueInternational Journal of Online Pedagogy and Course Design · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsArgumentation theoryContext (archaeology)Online discussionMeaning (existential)Dimension (graph theory)CognitionSet (abstract data type)EpistemologyCognitive dimensions of notationsDiscourse analysisPsychologyConstructivism (international relations)Collaborative learningComputer scienceKnowledge managementMathematics educationLinguisticsWorld Wide Web

Abstract

fetched live from OpenAlex

This study illustrates different practices of knowledge co-construction by exploring argumentation processes from its (1) cognitive, (2) affective, and (3) moral dimensions (respect), and by clarifying the meaning of cooperation and collaboration, terms that are commonly used as synonyms. The authors adopted a critical constructivist approach consistent with the cognitive and moral works of Habermas (1987) and Piaget (1977, 1932/2000), and refined a method of online argumentation analysis (Campos, 2004) to better understand knowledge co-construction in the context of electronic conferencing in university courses. Their data analysis focused on the form as well as on the content of online argumentation. Results concerning the cognitive dimension of online discourse confirmed previous studies. However, regarding the affective and moral (respect) dimensions of online discourse, results were less clear. The authors highlight that the technology, the course design, and the instructor’s actions are equally important to successfully achieve set goals in online learning communities.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.418
Teacher spread0.378 · 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 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

Citations3
Published2013
Admission routes1
Has abstractyes

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