Purpose: Since the COVID-19 pandemic, virtual simulation practice has been increasingly activated as an alternative to clinical practice in nursing colleges. This study aimed to provide basic data by confirming changes in self-efficacy and nursing knowledge in the virtual simulations of nursing students, and identifying virtual presence, virtual patient learning system evaluation (VPLSE), and practical satisfaction. Methods: This was a single-group pre-post quasi-experimental study. The subjects were 28 third-grade nursing students. Results: Self-efficacy and nursing knowledge increased significantly (p<.001). Virtual presence had a significant positive correlation with VPLSE) (p=.002) and practice satisfaction (p=.011). There was also a significant positive correlation between virtual simulation learning evaluation and practice satisfaction (p<.001). Conclusion: Based on these results, virtual simulation practice can be used with clinical practice as an educational method to improve nursing students' self-efficacy and nursing knowledge in nursing education. Virtual presence was confirmed as a significant variable to improve practice satisfaction and VPLSE. It is necessary to develop a virtual simulation program that can improve virtual presence through collaboration with virtual reality technology experts.

Three CNN (Convolutional Neural Network) models of GoogLeNet, VGGNet, and Alexnet were evaluated to select the best deep learning based image analysis mothod that can detect pavement distresses of pothole, spalling, and punchout on expressway. Education data was obtained using pavement surface images of 11,056km length taken by Gopro camera equipped with an expressway patrol car. Also, deep learning framework of Caffe developed by Berkeley Vision and Learning Center was evaluated to use the three CNN models with other frameworks of Tensorflow developed by Google, and CNTK developed by Microsoft. After determing the optimal CNN model applicable for the distress detection, the analyzed images and corresponding GPS locations, distress sizes (greater than distress length of 150mm), required repair material quantities are trasmitted to local maintenance office using LTE wireless communication system through ICT center in Korea Expressway Corporation. It was found out that the GoogLeNet, AlexNet, and VGG-16 models coupled with the Caffe framework can detect pavement distresses by accuracy of 93%, 86%, and 72%, respectively. In addition to four distress image groups of cracking, spalling, pothole, and punchout, 22 different image groups of lane marking, grooving, patching area, joint, and so on were finally classified to improve the distress detection rate.

  This study aims to design and implement a learning evaluation system using .NET which is developed by Microsoft. .NET technology supports higher processing speed than ASP technology. The learning evaluation system is based on the web, consists of admini