Posted: Oct 12, 2023
This research investigates the transformative potential of technology-mediated education in continuing professional nursing development through a novel methodological framework that combines neurocognitive assessment with adaptive learning analytics. Unlike previous studies that primarily focus on knowledge acquisition or skill demonstration, this research employs a multi-dimensional approach examining how different technological modalities influence cognitive load patterns, clinical decision-making pathways, and long-term knowledge retention among practicing nurses. The study introduces an innovative adaptive learning ecosystem that dynamically adjusts educational content based on real-time cognitive state monitoring and clinical context relevance. Our methodology integrates electroencephalography (EEG) measurements of cognitive load with eye-tracking analysis of information processing during simulated clinical scenarios, creating a comprehensive picture of how nurses engage with technology-mediated learning. Results from our longitudinal study with 245 practicing nurses across three healthcare systems reveal that contextually-adaptive learning systems significantly enhance clinical reasoning skills and reduce cognitive fatigue compared to traditional e-learning platforms. Furthermore, we demonstrate that personalized learning pathways generated through machine learning algorithms can predict and address individual competency gaps with 87
Downloads: 83
Abstract Views: 1967
Rank: 317375