Models for Building Knowledge in a Technology-Rich Setting: Teacher Education
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it