Evaluating Web-Based Teacher Development Projects: Models, Methods, and Outcomes
Bibliographic record
Abstract
This article focuses on evaluations of three Web-based projects aimed at developing various aspects of teachers’ professional competences. The first examines the use of a software tool to facilitate the creation of knowledge-building communities among preservice candidates, host teachers, and high school students. The second was an evaluation of two sites that participated in Canada’s SchoolNet GrassRoots program. A pilot video-on-demand service to high school teachers was the third project we evaluated. From an analysis of these three evaluation projects, we describe and discuss (a) project outcomes that promoted teacher knowledge-building, (b) contextual factors that influenced the success of Web-based professional development activities, and (c) methodological issues that emerged from the studies. Cet article met l’accent sur les évaluations de trois projets de développement de divers aspects de la compétences professionnelles des professeurs et utilisant principalement l’Internet. Le premier examine l’utilisation d’un logiciel-outil pour faciliter la création de communautés de construction de connaissances chez des étudiants en formation des maîtres, des professeurs invités et des étudiants de niveau secondaire. Le second est une évaluation de deux sites qui ont fait partie du programme canadien SchoolNet GrassRoots. Un service pilote de vidéo sur demande pour les enseignants du niveau secondaire était le troisième projet que nous avons évalué. À partir de l’analyse de ces trois projets d’évaluation, nous décrivons et discutons (a) les résultats et les facteurs qui ont promu la construction de connaissances chez les enseignants, (b) les facteurs contextuels qui influent sur le succès d’activités de développement professionnel utilisant principalement l’Internet, et (c) les questions méthodologiques qui ont émergées de ces études.
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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 | 0.065 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".