Models for Building Knowledge in a Technology-Rich Setting: Teacher Education
Bibliographic record
Abstract
Technology offers promising opportunities for creating new types of classroom learning environments. This paper describes three technology models used by teacher education interns: electronic portfolios, negotiative concept mapping, cognote-supported electronic discussions. As implemented in the current study, these models invoke graduated attributes of knowledge building and as such serve as a useful continuum of examples of the potential of technology to assist in promoting progressive knowledge construction. A description of the models is followed by a discussion of the relationship of these classrooms to Knowledge-Building principles. Résumé La technologie offre des possibilités prometteuses pour la création de nouveaux types d’environnements d’apprentissage en classe. Le présent article décrit trois modèles technologiques utilisés par les stagiaires en enseignement : portfolios électroniques, cartographie conceptuelle de négociation, discussions électroniques avec codage. Tels que mis en œuvre dans le cadre de la présente étude, ces modèles font appel à des attributs hiérarchiques de coélaboration des connaissances et constituent donc en eux-mêmes un continuum utile d’exemples illustrant comment la technologie peut aider à encourager l’élaboration progressive des connaissances. Une description des modèles est suivie d’une discussion portant sur la relation de ces classes avec les principes de coélaboration des connaissances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".