The Distance Education has evolved with the available technology in each new decade. The evolution and spread of mobile technology from year 2000s enabled their migration to this new platform: The Mobile Learning. Making it possible for professionals and students can carry with multimedia tools with Internet access to learning tools or professional references. This new concept fits very well the needs of Health, in which students must absorb and put into practice large amounts of technical knowledge, and also professionals must stay constantly updated. Distance Education in Health has received prominence in Brazil. A country of a geographically dispersed group of professionals, and research & training centers concentrated in the capitals. Updating field teams is a difficult task, but the information has access to modern technologies, which contribute to the teachers who use them. This paper, through the methodology of literature review, presents technology experiments in health environments and their considerations.

Download full-text PDF

Source

Publication Analysis

Top Keywords

mobile learning
8
distance education
8
brazilian experiments
4
experiments mobile
4
health
4
learning health
4
professionals
4
health professionals
4
professionals distance
4
education evolved
4

Similar Publications

Background: Mobile microlearning (MML) provides concise and engaging educational activities that correspond with various learning preferences and styles. Microlearning is defined as bite-sized instruction, with modules ranging from approximately 90 seconds to 5 minutes. To consider MML as a form of continuing professional development it is essential first to identify the learning preferences of a new generation of nurses entering the professional field of health care.

View Article and Find Full Text PDF

Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO, PM, and PM, and extracted features from ∼382,000 SVIs at multiple angles (0°, 90°, 180°, 270°) and buffer radii (100-500 m).

View Article and Find Full Text PDF

Single-pixel imaging (SPI) using deep learning networks, e.g., convolutional neural networks (CNNs) and vision transformers (ViTs), has made significant progress.

View Article and Find Full Text PDF

Background And Objective: Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!