Gender impact on STEM online learning- a correlational study of gender, personality traits and learning styles in relation to different online teaching modalities.

STEM (science, technology, engineering and mathematics) education benefits both individuals and society. It supports individuals by increasing their critical-thinking skills, encouraging creativity, as well as providing a basis for new inventions. The underrepresentation of women in STEM is a complex issue with various causes and different approaches of addressing it, where most likely gender differences are caused by desires and choice rather than abilities and performance. This paper explores differences in online and traditional STEM learning based on gender. It examines in detail recently identified patterns of women's success, their access to STEM online courses, and their overall course experience during such courses. We analyzed results from a case study in which students were enrolled for one semester in two STEM online courses and completed questionnaires about their character traits and learning styles and how they relate to academic performance. The objective of our research is to analyze academic success during traditional classes and online classes, with focus on gender and identify how character traits and learning styles correlate with gender in online classes. The main outcome of our research is that female students, which study in the field of STEM in particular computer science, are trustworthy and autonomous students who can outperform their male counterparts during traditional courses, where during online courses male students still exceed slightly female students. The trait of Consciousness is a success predictor regardless of gender and learning environment, while the trait of Neuroticism has negative impact the traditional learning environment, Extraversion shows negative impact in online learning. Learning styles show gender differences, where female students prefer the style of read/write while male students favor kinesthetic.

Rural Healthcare IoT Architecture Based on Low-Energy LoRa.

Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers' well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.