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Abstract: This book provides a brief history of web-based virtual reality and shows readers how to build three-dimensional web content for businesses using the A-Frame framework. Topics include building a basic scene with A-Frame primitives, applying textures and models, implementing navigation and collision detection, adding multiuser support, and integrating WebRTC for audio, video, and screen sharing. Features are illustrated using real-world business examples, including scenes for real estate, travel, retail, education, and enterprise. This book is intended for students and business practitioners with minimal programming experience who are interested in creating business-oriented virtual reality experiences on the web. It may also be used by educators looking to introduce web-based virtual reality concepts in a classroom setting.
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Abstract: Many researchers since the 1990s have attempted to determine the effect of using virtual reality in education. The same equipment used for entertainment purposes can be equipped by the educational system to provide new and immersive methods to traditional teaching. This present research proposes a multi-user, three-dimensional virtual environment (3DVE) that recreates an interactive, historically accurate, lesson-based scene to teach K-12 students. To ensure all students can access and participate in the learning experience, the 3DVE supports desktop, mobile, as well as fully immersive virtual reality head mounted displays (HMDs). In addition, a multi-user feature allows students to engage in a shared learning environment with the educator.
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Abstract: The rise of online courses due to the COVID-19 pandemic has led to the use of web conferencing tools such as Zoom, Microsoft Teams, and Blackboard Collaborate to conduct lectures. Research has found that classes that utilize these tools suffer from lack of student engagement. Prior studies have attempted to address this issue using three-dimensional virtual environments (3DVE) which have resulted in technical problems experienced by their participants. The purpose of this research is to provide and investigate the use of a 3D virtual classroom that integrates a 3DVE with web conferencing features to improve student engagement and resolve the technical issues reported in previous studies. Results show that students had no technical issues accessing and running the 3D classroom and felt more engaged in the class than when using Blackboard Collaborate. Furthermore, assessment scores of students that attended the class in the 3D classroom were on average over twice that of those that used Blackboard Collaborate.
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Abstract: Business analytics can be described as the statistical analysis of data to make decisions and meaningful conclusions. As the demand to advance the curriculum of undergraduate business education increases, courses in business analytics aim to provide students with fundamental skills in critical thinking. Educators have found that spreadsheet applications that include statistical features are easy to use and facilitate student learning. The authors analyzed student performance in an introductory business analytics course that used Microsoft Excel as a statistical tool by comparing scores from this introductory course with those from an information technology course in which only Excel skills were learned.
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Abstract: Business analytics has become an essential area of study for students looking to gain an understanding of the statistical techniques and specialized tools used to assist businesses in their decision-making process. As datasets become increasingly large and complex, traditional visualization techniques such as scatter, bar, and line charts often result in cluttered visualizations. Current three-dimensional visualizations solutions that overcome the spatial challenges presented by two-dimensional charts have led to the problems of data obscurity and user disorientation. Consequently, virtual reality technology has recently been considered as a potential alternative for visualizing large datasets. This research presents an R package for visualizing Excel data in virtual reality that transcends the limitations of current three-dimensional visualization offerings.
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Abstract: Since the turn of the century, there has been increased interest to apply virtual reality technology, used extensively in the field of gaming, to e-learning. Minecraft, a popular gaming platform among students, provides all of the elements needed to develop a virtual reality e-learning business simulation including non-player characters (NPCs), in-game scripting via command blocks, mechanisms to trigger these commands, and a scoreboard feature. This research in progress presents the stages of development required to build a virtual reality e-learning business simulation using Minecraft, and demonstrates the advantages of using Minecraft over other popular virtual reality applications for constructing three-dimensional virtual environments for e-learning.
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Abstract: Games and simulations have been a used to facilitate the student learning experience in the classroom for several decades. Business simulations date back to the 1950s, and since the late 1990s, computer-based behavioral simulations featuring the use of decision trees have become a standard form of e-learning. Recent studies have applied virtual reality to e-learning simulations in the areas of computer science, engineering, mathematics, medicine, and social science. This research in progress presents a virtual reality decision tree simulation that will aid business students in the learning of management information systems just as similar systems have done for other fields of study in the past.
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Abstract: The Problem: Given the significant e-learning growth over the next decade, autonomic assessments are required to gain efficiencies and allow greater focus on learning. While capabilities are increasing there are still a number of challenges and limited application framework for utilizing autonomic assessments within an e-learning environment.
What Was Done: This poster paper develops an application framework for autonomic assessments in the context of an overall learning environment, where e-learning applications are anticipated to grow at an increasing rate over the next decade.
Why this Work is Important: E-learning entities and platforms have been under increased pressure due to critics and government based on completion rates, costs, and student readiness. New technologies such as autonomic assessments can be leveraged to further enhance e-learning platforms and improve completion, reduce costs, and improve learning outcomes for students.
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Woodside, J. M., Amiri, S., &
Sause, W. (2015, October).
The internet of things in e-learning. In
eLearn: World Conference on EdTech (pp. 1207-1210). Association for the Advancement of Computing in Education (AACE).
Abstract: The Problem: The Internet of Things (IoT) is the next phase of innovation technology and educational applications over the next decade. While capabilities are increasing there are still a number of challenges and limited application framework for utilizing these technologies within an e-learning environment.
What Was Done: This work in progress showcase paper develops an application framework for the underlying technologies, contexts, and services that can be developed in e-learning, where IoT applications are anticipated to grow at an increasing rate over the next decade.
Why this Work is Important: E-learning entities and platforms have been under increased pressure due to critics and government based on completion rates, costs, employment, and career readiness. New technologies and IoT applications can be leveraged to further enhance e-learning platforms and improve completion, reduce costs, and improve learning outcomes for careers.
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Abstract: This research presents a framework for coordinating multiple intelligent agents within a single virtual environment. Coordination is accomplished via a "next available agent" scheme while learning is achieved through the use of the Q-learning and Sarsa temporal difference reinforcement learning algorithms. To assess the effectiveness of each learning algorithm, experiments were conducted that measured an agent's ability to learn tasks in a static and dynamic environment while using both a fixed (FEP) and variable (VEP) ϵ-greedy probability rate. Results show that Sarsa, on average, outperformed Q-learning in almost all experiments. Overall, VEP resulted in higher percentages of successes and optimal successes than FEP, and showed convergence to the optimal policy when measuring the average number of time steps per episode.
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Abstract: The growing popularity of online virtual communities such as Second Life and ActiveWorlds demands the presence of intelligent agents to assist users in their daily online activities (e.g., exploring, shopping, and socializing). As these virtual environments become more crowded, multiple agents are needed to support the increasing number of users. Multi-agent environments, however, can suffer from the problem of resource competition among agents. It is therefore necessary that agents within multi-agent environments include a coordination mechanism to prevent unrealistic behaviors. Moreover, it is essential that these agents exhibit some form of intelligence, or the ability to learn, to support realism as well as to eliminate the need for developers to write separate scripts for each task the agents are required to perform. This research presents a coordinated reinforcement learning framework which can be used to develop task-oriented intelligent agents in multi-agent virtual environments. The framework contains a combination of a "next available agent" coordination model and a reinforcement learning model consisting of existing temporal difference reinforcement learning algorithms. Furthermore, the framework supports evaluations of reinforcement learning algorithms to determine which methods are best suited for task-oriented intelligent agents in dynamic, multi-agent virtual environments.
To assess the effectiveness of the temporal difference reinforcement algorithms used in this study (Q-learning and Sarsa), experiments were conducted that measured an agent's ability to learn three tasks commonly performed by workers in a café environment. These tasks were basic sandwich making (BSM), complex sandwich making (CSM), and dynamic sandwich making (DSM). The BSM task consisted of four steps. The CSM and DSM tasks contained an additional fifth step. The agent learned the BSM and CSM tasks from scratch while the DSM task was learned after the agent became skillful in BSM. The measurements used to evaluate the efficiency of the Q-learning and Sarsa algorithms were the percentage of successful and optimally successful episodes performed by the agent and the average number of time steps taken by the agent to complete a successful episode. The experiments were run using both a fixed (FEP) and variable (VEP) ε-greedy probability rate. Results showed that the Sarsa reinforcement learning algorithm, on average, outperformed the Q-learning algorithm in almost all experiments except when measuring the percentage of successfully completed episodes using FEP for CSM and DSM, in which Sarsa performed almost equally as well as Q-learning. Overall, experiments utilizing VEP resulted in higher percentages of successes and optimal successes, and showed convergence to the optimal policy when measuring the average number of time steps per successful episode.