Pub Date : 2019-12-01DOI: 10.1109/AIVR46125.2019.00014
Ryo Takizawa, Adrien Verhulst, Katie Seaborn, M. Fukuoka, Atsushi Hiyama, M. Kitazaki, M. Inami, M. Sugimoto
With advancements in robotics, systems featuring wearable robotic arms teleoperated by a third party are appearing. An important aspect of these systems is the visual feedback provided to the third party operator. This can be achieved by placing a wearable camera on the robotic arm's "host", but such a setup makes the visual feedback dependent on the movements of the main body. Here we reproduced this view dependency in VR using a shared body. The "host" shares their virtual body with the virtual Supernumerary Robotic Arms of the teleoperator. In our research, the two users perform two tasks: (i) a "synchronization task" to improve their joint action performance and (ii) a "building task" where they worked together to build a tower. In a user study, we evaluated the embodiment, workload, and performance of the teleoperator through the "building task" with three different view dependency modes. We present some early outcomes as trends that might give directions for the future investigations.
{"title":"Exploring Perspective Dependency in a Shared Body with Virtual Supernumerary Robotic Arms","authors":"Ryo Takizawa, Adrien Verhulst, Katie Seaborn, M. Fukuoka, Atsushi Hiyama, M. Kitazaki, M. Inami, M. Sugimoto","doi":"10.1109/AIVR46125.2019.00014","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00014","url":null,"abstract":"With advancements in robotics, systems featuring wearable robotic arms teleoperated by a third party are appearing. An important aspect of these systems is the visual feedback provided to the third party operator. This can be achieved by placing a wearable camera on the robotic arm's \"host\", but such a setup makes the visual feedback dependent on the movements of the main body. Here we reproduced this view dependency in VR using a shared body. The \"host\" shares their virtual body with the virtual Supernumerary Robotic Arms of the teleoperator. In our research, the two users perform two tasks: (i) a \"synchronization task\" to improve their joint action performance and (ii) a \"building task\" where they worked together to build a tower. In a user study, we evaluated the embodiment, workload, and performance of the teleoperator through the \"building task\" with three different view dependency modes. We present some early outcomes as trends that might give directions for the future investigations.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130036901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/AIVR46125.2019.00019
J. Orlosky, Brandon Huynh, Tobias Höllerer
In recent years, augmented and virtual reality (AR/VR) have started to take a foothold in markets such as training and education. Although AR and VR have tremendous potential, current interfaces and applications are still limited in their ability to recognize context, user understanding, and intention, which can limit the options for customized individual user support and the ease of automation. This paper addresses the problem of automatically recognizing whether or not a user has an understanding of a certain term, which is directly applicable to AR/VR interfaces for language and concept learning. To do so, we first designed an interactive word recall task in VR that required non-native English speakers to assess their knowledge of English words, many of which were difficult or uncommon. Using an eye tracker integrated into the VR Display, we collected a variety of eye movement metrics that might correspond to the user's knowledge or memory of a particular word. Through experimentation, we show that both eye movement and pupil radius have a high correlation to user memory, and that several other metrics can also be used to help classify the state of word understanding. This allowed us to build a support vector machine (SVM) that can predict a user's knowledge with an accuracy of 62% in the general case and and 75% for easy versus medium words, which was tested using cross-fold validation. We discuss these results in the context of in-situ learning applications.
{"title":"Using Eye Tracked Virtual Reality to Classify Understanding of Vocabulary in Recall Tasks","authors":"J. Orlosky, Brandon Huynh, Tobias Höllerer","doi":"10.1109/AIVR46125.2019.00019","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00019","url":null,"abstract":"In recent years, augmented and virtual reality (AR/VR) have started to take a foothold in markets such as training and education. Although AR and VR have tremendous potential, current interfaces and applications are still limited in their ability to recognize context, user understanding, and intention, which can limit the options for customized individual user support and the ease of automation. This paper addresses the problem of automatically recognizing whether or not a user has an understanding of a certain term, which is directly applicable to AR/VR interfaces for language and concept learning. To do so, we first designed an interactive word recall task in VR that required non-native English speakers to assess their knowledge of English words, many of which were difficult or uncommon. Using an eye tracker integrated into the VR Display, we collected a variety of eye movement metrics that might correspond to the user's knowledge or memory of a particular word. Through experimentation, we show that both eye movement and pupil radius have a high correlation to user memory, and that several other metrics can also be used to help classify the state of word understanding. This allowed us to build a support vector machine (SVM) that can predict a user's knowledge with an accuracy of 62% in the general case and and 75% for easy versus medium words, which was tested using cross-fold validation. We discuss these results in the context of in-situ learning applications.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131125467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/AIVR46125.2019.00015
Atiqul Islam, Jinshuai Ma, Tom Gedeon, Md. Zakir Hossain, Ying-Hsang Liu
The use of simulator technology has become popular in providing training, investigating driving activity and performing research as it is a suitable alternative to actual field study. The transferability of the achieved result from driving simulators to the real world is a critical issue considering later real-world risks, and important to the ethics of experiments. Moreover, researchers have to trade-off between simulator sophistication and the cost it incurs to achieve a given level of realism. This study will be the first step towards investigating the plausibility of different driving simulator configurations of varying verisimilitude, from drivers' galvanic skin response (GSR) signals. GSR is the widely used indicator of behavioural response. By analyzing GSR signals in a simulation environment, our results are aimed to support or contradict the use of simple low-level driving simulators. We investigate GSR signals of 23 participants doing virtual driving tasks in 5 different configurations of simulation environments. A number of features are extracted from the GSR signals after data preprocessing. With a simple neural network classifier, the prediction accuracy of different simulator configurations reaches up to 90% during driving. Our results suggest that participants are more engaged when realistic controls are used in normal driving, and are less affected by visible context during driving in emergency situations. The implications for future research are that for emergency situations realistic controls are important and research can be conducted with simple simulators in lab settings, whereas for normal driving the research should be conducted with full context in a real driving setting.
{"title":"Measuring User Responses to Driving Simulators: A Galvanic Skin Response Based Study","authors":"Atiqul Islam, Jinshuai Ma, Tom Gedeon, Md. Zakir Hossain, Ying-Hsang Liu","doi":"10.1109/AIVR46125.2019.00015","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00015","url":null,"abstract":"The use of simulator technology has become popular in providing training, investigating driving activity and performing research as it is a suitable alternative to actual field study. The transferability of the achieved result from driving simulators to the real world is a critical issue considering later real-world risks, and important to the ethics of experiments. Moreover, researchers have to trade-off between simulator sophistication and the cost it incurs to achieve a given level of realism. This study will be the first step towards investigating the plausibility of different driving simulator configurations of varying verisimilitude, from drivers' galvanic skin response (GSR) signals. GSR is the widely used indicator of behavioural response. By analyzing GSR signals in a simulation environment, our results are aimed to support or contradict the use of simple low-level driving simulators. We investigate GSR signals of 23 participants doing virtual driving tasks in 5 different configurations of simulation environments. A number of features are extracted from the GSR signals after data preprocessing. With a simple neural network classifier, the prediction accuracy of different simulator configurations reaches up to 90% during driving. Our results suggest that participants are more engaged when realistic controls are used in normal driving, and are less affected by visible context during driving in emergency situations. The implications for future research are that for emergency situations realistic controls are important and research can be conducted with simple simulators in lab settings, whereas for normal driving the research should be conducted with full context in a real driving setting.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115279300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/AIVR46125.2019.00066
Ludovic Hoyet, Clément Spies, Pierre Plantard, A. Sorel, R. Kulpa, F. Multon
For fullbody interaction, avatar simulation and control involves several steps that leads to delay (or latency) between the users' and their avatar's motion. Previous works have shown an impact of this delay on the perception-action loop, with possible impact on Presence and embodiment. In this paper we explore how the speed of the motion can impact the way people perceive and react to such a delay. We conducted an experiment where users were asked to follow a moving object with their finger, while embodied in a realistic avatar. We artificially increased the latency (up to 300ms) and measured their performance in the mentioned task.Our results show that motion speed influenced the perception of latency: we found critical latencies of 80ms for medium and fast motion speeds, while the critical latency reached 120ms for a slow motion speed. We also found that performance was affected by latency before the critical latency for medium and fast speeds, but not for a slower speed.
{"title":"Influence of Motion Speed on the Perception of Latency in Avatar Control","authors":"Ludovic Hoyet, Clément Spies, Pierre Plantard, A. Sorel, R. Kulpa, F. Multon","doi":"10.1109/AIVR46125.2019.00066","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00066","url":null,"abstract":"For fullbody interaction, avatar simulation and control involves several steps that leads to delay (or latency) between the users' and their avatar's motion. Previous works have shown an impact of this delay on the perception-action loop, with possible impact on Presence and embodiment. In this paper we explore how the speed of the motion can impact the way people perceive and react to such a delay. We conducted an experiment where users were asked to follow a moving object with their finger, while embodied in a realistic avatar. We artificially increased the latency (up to 300ms) and measured their performance in the mentioned task.Our results show that motion speed influenced the perception of latency: we found critical latencies of 80ms for medium and fast motion speeds, while the critical latency reached 120ms for a slow motion speed. We also found that performance was affected by latency before the critical latency for medium and fast speeds, but not for a slower speed.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"IA-17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114119869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/AIVR46125.2019.00025
Junya Ueda, K. Okajima
We propose an AR application that enables us to change the appearance of food without AR markers by applying machine learning and image processing. Modifying the appearance of real food is a difficult task because the shape of the food is atypical and deforms while eating. Therefore, we developed a real-time object region extraction method that combines two approaches in a complementary manner to extract food regions with high accuracy and stability. These approaches are based on color and edge information processing with a deep learning module trained with a small amount of data. Besides, we implemented some novel methods to improve the accuracy and reliability of the system. Then, we experimented and the results show that the taste and oral texture were affected by visual textures. Our application can change not only the appearance in real-time but also the taste and texture of actual real food. Therefore, in conclusion, our application can be virtually termed as an "AR food changer".
{"title":"AR Food Changer using Deep Learning And Cross-Modal Effects","authors":"Junya Ueda, K. Okajima","doi":"10.1109/AIVR46125.2019.00025","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00025","url":null,"abstract":"We propose an AR application that enables us to change the appearance of food without AR markers by applying machine learning and image processing. Modifying the appearance of real food is a difficult task because the shape of the food is atypical and deforms while eating. Therefore, we developed a real-time object region extraction method that combines two approaches in a complementary manner to extract food regions with high accuracy and stability. These approaches are based on color and edge information processing with a deep learning module trained with a small amount of data. Besides, we implemented some novel methods to improve the accuracy and reliability of the system. Then, we experimented and the results show that the taste and oral texture were affected by visual textures. Our application can change not only the appearance in real-time but also the taste and texture of actual real food. Therefore, in conclusion, our application can be virtually termed as an \"AR food changer\".","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129314171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/AIVR46125.2019.00017
A. M. Nascimento, A. B. M. Queiroz, L. Vismari, J. Bailenson, P. Cugnasca, J. B. C. Junior, J. R. Almeida
Virtual Reality (VR) has played an important role in the development of autonomous robots. From Computer Aided Design (CAD) to simulators for testing automation algorithms without risking expensive equipment, VR has been used in a wide range of applications. Most recently, Autonomous Vehicles (AV), a special application of autonomous robots, became a major focus of the scientific and practitioner community for road and vehicle safety improvements. However, recent AV accidents shed a light on the new safety challenges that need be addressed to fulfill those safety expectations. This paper presents a systematic literature mapping on the use of VR for AV safety and assimilates this literature to create a vision of how VR will play an important role in the development of safety in AV.
{"title":"The Role of Virtual Reality in Autonomous Vehicles’ Safety","authors":"A. M. Nascimento, A. B. M. Queiroz, L. Vismari, J. Bailenson, P. Cugnasca, J. B. C. Junior, J. R. Almeida","doi":"10.1109/AIVR46125.2019.00017","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00017","url":null,"abstract":"Virtual Reality (VR) has played an important role in the development of autonomous robots. From Computer Aided Design (CAD) to simulators for testing automation algorithms without risking expensive equipment, VR has been used in a wide range of applications. Most recently, Autonomous Vehicles (AV), a special application of autonomous robots, became a major focus of the scientific and practitioner community for road and vehicle safety improvements. However, recent AV accidents shed a light on the new safety challenges that need be addressed to fulfill those safety expectations. This paper presents a systematic literature mapping on the use of VR for AV safety and assimilates this literature to create a vision of how VR will play an important role in the development of safety in AV.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129321492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/AIVR46125.2019.00065
Nadisha-Marie Aliman, L. Kester, P. Werkhoven
Steady progresses in the AI field create enriching possibilities for society while simultaneously posing new complex challenges of ethical, legal and safety-relevant nature. In order to achieve an efficient human-centered governance of artificial intelligent systems, it has been proposed to harness augmented utilitarianism (AU), a novel non-normative ethical framework grounded in science which can be assisted e.g. by Extended Reality (XR) technologies. While AU provides a scaffold to encode human ethical and legal conceptions in a machine-readable form, the filling in of these conceptions requires a transdisciplinary amalgamation of scientific insights and preconditions from manifold research areas. In this short paper, we present a compact review on how XR technologies could leverage the underlying transdisciplinary AI governance approach utilizing the AU framework. Towards that end, we outline pertinent needs for XR in two hereto related contexts: as experiential testbed for AU-relevant moral psychology studies and as proactive AI Safety measure and enhancing policy-by-simulation method preceding the deployment of AU-based ethical goal functions.
{"title":"XR for Augmented Utilitarianism","authors":"Nadisha-Marie Aliman, L. Kester, P. Werkhoven","doi":"10.1109/AIVR46125.2019.00065","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00065","url":null,"abstract":"Steady progresses in the AI field create enriching possibilities for society while simultaneously posing new complex challenges of ethical, legal and safety-relevant nature. In order to achieve an efficient human-centered governance of artificial intelligent systems, it has been proposed to harness augmented utilitarianism (AU), a novel non-normative ethical framework grounded in science which can be assisted e.g. by Extended Reality (XR) technologies. While AU provides a scaffold to encode human ethical and legal conceptions in a machine-readable form, the filling in of these conceptions requires a transdisciplinary amalgamation of scientific insights and preconditions from manifold research areas. In this short paper, we present a compact review on how XR technologies could leverage the underlying transdisciplinary AI governance approach utilizing the AU framework. Towards that end, we outline pertinent needs for XR in two hereto related contexts: as experiential testbed for AU-relevant moral psychology studies and as proactive AI Safety measure and enhancing policy-by-simulation method preceding the deployment of AU-based ethical goal functions.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121802548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.1109/AIVR46125.2019.00042
V. Nguyen, Kwanghee Jung, Tommy Dang
With the advancement of modern technologies, Virtual Reality plays an essential role for training rescuers, particularly for disaster savers employing simulation training. By wholly immersed in the virtual environment, rescuers are capable of practicing the required skills without being threatened of their lives before experiencing the real world situation. This paper presented a work-in-progress Virtual Reality application called VRescuer to help trainees get used to various disaster circumstances. A scenario of a city was created with an ambulance rescuer and several rescuees in the scene. The intelligent ambulance rescuer was introduced as a rescuer/guider to automatically search and find the optimal paths for saving all rescuees. The trainee can interfere in the rescuing process by placing obstacles or adding more rescuees along the ways which cause the rescue agent to re-route the paths. The VRescuer was implemented in Unity3D with an Oculus Rift device, and it was assessed by twenty users to improve the proposed application.
{"title":"VRescuer: A Virtual Reality Application for Disaster Response Training","authors":"V. Nguyen, Kwanghee Jung, Tommy Dang","doi":"10.1109/AIVR46125.2019.00042","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00042","url":null,"abstract":"With the advancement of modern technologies, Virtual Reality plays an essential role for training rescuers, particularly for disaster savers employing simulation training. By wholly immersed in the virtual environment, rescuers are capable of practicing the required skills without being threatened of their lives before experiencing the real world situation. This paper presented a work-in-progress Virtual Reality application called VRescuer to help trainees get used to various disaster circumstances. A scenario of a city was created with an ambulance rescuer and several rescuees in the scene. The intelligent ambulance rescuer was introduced as a rescuer/guider to automatically search and find the optimal paths for saving all rescuees. The trainee can interfere in the rescuing process by placing obstacles or adding more rescuees along the ways which cause the rescue agent to re-route the paths. The VRescuer was implemented in Unity3D with an Oculus Rift device, and it was assessed by twenty users to improve the proposed application.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134161081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-26DOI: 10.1109/AIVR46125.2019.00045
V. Nguyen, Kwanghee Jung, Tommy Dang
The fast-growing number of high-performance computer processor and hand-held devices have paved the way for the development of Virtual Reality and Augmented Reality in terms of hardware and software in the education sector. The question of whether students can adopt these new technologies is not fully addressed. Answering this question thus plays an essential role for instructors and course designers. The objectives of this study are: (1) to investigate the feasibility of the Virtual Reality/Augmented Reality development for undergraduate students, and (2) to highlight some practical challenges when creating and sharing Virtual Reality and Augmented Reality applications from student's perspective. Study design for the coursework was given with detail. During a 16-week long, 63 Virtual Reality/Augmented Reality applications were created from a variety of topics and various development tools. 43 survey questions are prepared and administered to students for each phase of the projects to address technical difficulties. The exploration method was used for data analysis.
{"title":"Creating Virtual Reality and Augmented Reality Development in Classroom: Is it a Hype?","authors":"V. Nguyen, Kwanghee Jung, Tommy Dang","doi":"10.1109/AIVR46125.2019.00045","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00045","url":null,"abstract":"The fast-growing number of high-performance computer processor and hand-held devices have paved the way for the development of Virtual Reality and Augmented Reality in terms of hardware and software in the education sector. The question of whether students can adopt these new technologies is not fully addressed. Answering this question thus plays an essential role for instructors and course designers. The objectives of this study are: (1) to investigate the feasibility of the Virtual Reality/Augmented Reality development for undergraduate students, and (2) to highlight some practical challenges when creating and sharing Virtual Reality and Augmented Reality applications from student's perspective. Study design for the coursework was given with detail. During a 16-week long, 63 Virtual Reality/Augmented Reality applications were created from a variety of topics and various development tools. 43 survey questions are prepared and administered to students for each phase of the projects to address technical difficulties. The exploration method was used for data analysis.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128343298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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