Teacher Education and Professional Development: 10 Years of ICT Integration and What?
Bibliographic record
Abstract
This paper reports on designs for effective uses of ICTs in teaching and learning in teaching education. Applying Engeström's schema (1987) at three different levels of use of the computer as a cultural tool, three sociocultural accounts, each one reflecting a different design activity for the betterment of teacher learning environments, were constructed. For each, clusters of interactions at the university–school partnership, networked classroom, and virtual collaborative space levels are described. As the history of each activity is presented, key features of the designed learning environments stand out, and trajectories of student teachers, teachers, and schools are highlighted.\n=== === RESUMEN=== ===\nEste artículo reporta acerca de los diseños para usos efectivos de las tecnologías de la información y la comunicación (TIC) en la enseñanza y el aprendizaje en la formación del profesorado. Mediante la aplicación del esquema de Engeström's (1987), que distinguen tres niveles de uso de la computadora como herramienta cultural, se econtraron tres motivos socioculturales de su utilización, cada uno de los cuales refleja un diseño distinto para la mejora de los ambientes de aprendizaje del profesorado. Para cada conjunto de interacción se describen niveles: el trabajo conjunto entre escuelas y universidad; aulas enlazadas por computadora, y espacios de colaboración virtual. Al presentar la experiencia de cada actividad, se destacan elementos clave de los diferentes ambientes de aprendizaje diseñados y se resaltan trayectorias de profesores en formación, profesores y escuelas.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".