Designing and delivering digital learning (e-Learning) interventions in nursing and midwifery education: A systematic review of theories
Notice bibliographique
Résumé
AIMS /OBJECTIVES: To identify and synthesise theories that support the design and delivery of digital learning interventions in nursing and midwifery education. BACKGROUND: A range of educational and other theories are used to support nursing and midwifery education, including when e-learning interventions are being designed and delivered. However, there is a limited understanding of how theory is applied across the wide range of digital learning interventions to inform pedagogical research and practice. DESIGN: A systematic review. METHODS: CINAHL, ERIC, MEDLINE and PubMed were searched using key terms. Studies were screened by independent reviewers checking the title, abstract and full text against eligibility criteria. Due to the theoretical focus of the review, critical appraisal was not undertaken. Data were extracted and synthesised using a descriptive approach. RESULTS: Thirty-four studies were included. Twenty theories were identified from a range of scientific disciplines, with the Technology Acceptance Model and Theory of Self-Efficacy employed most often. Theoretical frameworks were used to inform and explain how the digital learning interventions were designed or implemented in nursing and midwifery education. The sample were mainly undergraduate nursing students and the digital learning interventions encompassed animation, blended approaches, general technologies, mobile, online, virtual simulation and virtual reality applications which were used mainly in university settings. CONCLUSIONS: This systematic review found a range of theories that support the design and delivery on digital learning interventions in nursing and midwifery education. While a single theory, the Technology Acceptance Model, tended to dominate the literature, the evidence base is peppered with numerous theoretical models that need to be examined more rigorously to ascertain their utility in improving the design or implementation of digital forms of learning to improve pedagogical research and practice in nursing and midwifery.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».