Theories informing technology enhanced learning in nursing and midwifery education: A systematic review and typological classification
Notice bibliographique
Résumé
BACKGROUND: Learning is a complex process involving internal cognitive processes and external stimuli from curricula, pedagogical strategies, and the learning environment. Theories are used extensively in higher education to understand the intricacies of adult learning and improve student outcomes. Nursing and midwifery education uses a range of technology enhanced learning (e-learning) approaches, some of which are underpinned by theoretical frameworks. OBJECTIVE: Synthesise literature on theories that inform technology enhanced learning in nursing and midwifery education. DESIGN: A systematic review. DATA SOURCE: CINAHL, ERIC, MEDLINE and PubMed were searched for relevant studies (2000-2021). Reference lists of related literature reviews were hand searched. REVIEW METHODS: Title and abstract, followed by full texts were screened by two reviewers independently using predefined eligibility criteria. Quality appraisal was not undertaken. Data were extracted and Merriam and Bierema's typology of adult learning theories used to categorise theories in each study. RESULTS: Thirty-three studies were included, incorporating twenty-nine distinct learning theories from the behaviourist, cognitivist, constructivist, and social cognitivist domains, with constructivist being the most widely used. Kolb's Experiential Learning Theory and Driscoll's Constructivist Learning Theory were the most commonly reported theories. The population of learners were mainly undergraduate nursing students who used a range of online, mobile, blended or computerised learning, virtual reality, or digital forms of simulation, primarily in university settings. Theories were employed to inform the technology enhanced learning intervention or to help explain how these could improve student learning. CONCLUSION: This review highlighted a range of theories, particularly constructivist approaches, that underpin research on technology enhanced learning in nursing education, by informing or explaining how these digital interventions support learning. More rigorous research that examines the myriad of theoretical frameworks and their effectiveness in informing and explaining technology enhanced learning is needed to justify this approach to pedagogical nursing research and practice.
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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 | 0,001 | 0,002 |
| 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 | 0,000 |
| 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 ».