Building Teaching–Learning Capacities of Online Nurse Educators: Using TPACK to Frame Pedagogical Processes and Identify Required Supports | Renforcer les capacités d’enseignement et d’apprentissage des éducateurs en ligne en soins infirmiers : utiliser le modèle TPACK pour encadrer les processus pédagogiques et repérer les soutiens requis
Bibliographic record
Abstract
Quality teaching includes reflective practice, dialogue, and curiosity and depends on personal, institutional, and community assets and constraints, as well as on the individual’s definitions of the roles of education in society. Based on an environmental scan and action research with instructors in an online distance Bachelor of Science, Nursing program for registered nurses, participants identified five “big ideas” involving community, instructors, class, interpersonal relationships, and supports to build capacity around the three elements of technological pedagogical content knowledge, or TPACK (Koehler, Mishra, Akcaoglu, & Rosenberg, 2013).Un enseignement de qualité inclut une pratique réflexive, un dialogue et de la curiosité. Il dépend d’atouts et de contraintes personnelles, institutionnelles et communautaires, ainsi que de la façon dont chaque personne définit les rôles de l’éducation dans la société. En s’appuyant sur une analyse environnementale et une recherche-action auprès des instructeurs d’un programme de soins infirmiers pour infirmiers autorisés dans un baccalauréat en sciences en ligne et à distance, les participants ont repéré cinq « grandes idées » relatives à la collectivité, aux instructeurs, aux relations interpersonnelles et aux soutiens pour renforcer les capacités concernant les trois éléments du contenu technologique pédagogique, ou TPACK (Koehler, Mishra, Akcaoglu, et Rosenberg, 2013).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".