Bibliographic record
Abstract
This action research study examines the integration of K-12 online learning curriculum in a graduate instructional technology course aimed at preparing in-service teachers for online teaching environments. Through a cycle of curriculum implementation, data collection, and analysis, the study highlights the evolution of teachers' perceptions, identifying both the benefits and challenges of online education. Findings suggest that curriculum changes can significantly impact teachers' understanding and attitudes, though the study is limited by its small sample size and single-site context. Despite these limitations, the study offers valuable insights for teacher education programs seeking to incorporate online teaching components. Future research should consider expanding to multiple sites and updating curriculum content to reflect post-pandemic experiences in digital learning environments. Keywords: teacher education, online learning, higher education innovation, digital learning curriculum, graduate education, instructional technology, online learning curriculum Repenser la formation des enseignants : L’impact d’un programme d’apprentissage en ligne, pour les niveaux préscolaire, primaire et secondaire, sur les enseignants en exercice Résumé: Cette recherche-action étudie l'intégration d'un programme d'apprentissage en ligne allant du préscolaire au secondaire dans un cours de technologie éducative de niveau supérieur destiné à préparer les enseignants en exercice aux environnements d'enseignement en ligne. À travers un cycle d'implémentation du programme, de collecte et d'analyse de données, l'étude met en lumière l'évolution des perceptions des enseignants, identifiant à la fois les avantages et les défis de l'éducation en ligne. Les résultats suggèrent que les modifications curriculaires peuvent influencer significativement la compréhension et les attitudes des enseignants. Bien que l'étude soit limitée par la taille réduite de son échantillon et son contexte unique, elle offre des perspectives précieuses pour les programmes de formation des enseignants souhaitant intégrer des composantes d'enseignement en ligne. Les recherches futures devraient envisager d'étendre l'étude à plusieurs sites et de mettre à jour le contenu du programme pour refléter les expériences post-pandémiques dans les environnements d'apprentissage numérique. Mots-clés : formation des enseignants, apprentissage en ligne, innovation dans l'enseignement supérieur, programme d'apprentissage numérique, formation supérieure, technologie éducative, programme d'apprentissage en ligne
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".