In the education process, students face problems with understanding due to the complexity, necessity of abstract thinking and concepts. More and more educational centres around the world have started to introduce powerful new technology-based tools that help meet the needs of the diverse student population. Over the last several years, virtual reality (VR) has moved from being the purview of gaming to professional development. It plays an important role in teaching process, providing an interesting and engaging way of acquiring information. What follows is an overview of the big trend, opportunities and concerns associated with VR in education. We present new opportunities in VR and put together the most interesting, recent virtual reality applications used in education in relation to several education areas such as general, engineering and health-related education. Additionally, this survey contributes by presenting methods for creating scenarios and different approaches for testing and validation. Lastly, we conclude and discuss future directions of VR and its potential to improve the learning experience.Information 2019, 10, 318 2 of 20 must be carried out under supervision; therefore, students cannot self-configure lab equipment, experience states of emergency or effects of misconfiguration which may lead to equipment damage. Moreover, there is no possibility to practice and catch up outside the laboratory schedule. Currently, the solutions are modern technologies such as online courses [8,9], blended learning [10-13], different computer-based platforms [14-18] and many others, which allow the students to repeat several times the same topic, make mistakes and learn from them. Numerous examples of hardware and software which have been successful in educational processes indicate that edtech industry can improve learning outcomes for the majority of students [19]. More and more educational centres around the world are starting to introduce powerful new technology tools that help them to meet the needs of diverse student populations. Traditional books are being replaced by digital instructional content (especially from open educational resources) [20]. Notebooks, tablets or cell phones with dedicated application have replaced classical copybooks [21]. Distance [22] and personalised learning [23] are used to tailor education to each student's academic strengths, weaknesses, preferences and goals.It is well known that the use of information and communication technologies have been found to improve student attitudes towards learning [24][25][26][27]. It is a rapidly growing field of research, continually developing and looking for new technological solutions. Over the last several years, Virtual Reality (VR), which provides an interactive computer-generated environment, has moved from being the purview of the gaming to the professional development such as military, psychology, medicine and teaching applications.In 1987, Jaron Lanier, together with Steve Bryson, formulated the first definition of VR, which they desc...

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Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.