Evaluating Online Environments for Elementary Teachers’ Literacy-Oriented Professional Learning
Bibliographic record
Abstract
As elementary teachers increasingly turn to online environments for their literacy-oriented professional learning, evaluating website quality is of growing importance. Using screen capture recordings of participants’ navigations, the purpose of this study was to identify the types of online learning environments that elementary teachers use to enhance their literacy practice as well as to evaluate website quality. Findings reveal that teachers access ten main types of online environments. Those that were resource-based were accessed with the highest frequency despite having the lowest quality. Implications for the design of online learning environments as well as self-directed learning are explored in depth. Keywords: teacher professional development; teacher professional learning; self-directed learning; website evaluation; literacy Comme les enseignants du primaire se tournent de plus en plus vers les environnements en ligne pour leur apprentissage professionnel axé sur la littératie, l'évaluation de la qualité des sites Web revêt une importance croissante. Reposant sur des enregistrements de captures d'écran de la navigation des participants, l'objectif de cette étude était d'identifier les types d'environnements d'apprentissage en ligne que les enseignants du primaire utilisent pour améliorer leur pratique de l'alphabétisation ainsi que d'évaluer la qualité des sites Web. Les résultats révèlent que les enseignants accèdent à dix types principaux d'environnements en ligne. Ceux qui sont basés sur les ressources sont les plus utilisés, même si leur qualité est la plus faible. Les implications pour la conception d'environnements d'apprentissage en ligne ainsi que pour l'apprentissage autodirigé sont explorées en profondeur. Mots clés : développement professionnel des enseignants ; apprentissage professionnel des enseignants ; apprentissage autodirigé ; évaluation des sites Web ; alphabétisation.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".