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Simulation can be employed as an interactive computer game to enable game-based learning. Educational simulations can also be combined with immersive technologies such as virtual reality (VR) to enhance student engagement and learning. While recent years have seen significant growth in the use of immersive technologies in education, the role and contribution of the additional immersion offered by VR still needs to be explored. This paper aims to address this gap by comparing low- and high-immersion modes for a simulation game to familiarize students with the fundamental concepts of mathematical optimization. The game resembles performing a heuristic search on the solution space for an optimization problem and involves finding the highest peak in an arctic landscape. Our research experiments include three groups of students who play the game either in VR, desktop mode, or PowerPoint slides. Our statistical comparisons show that VR enhanced students' sense of presence and learning. 

With the increasing use of virtual simulated environments and immersive technologies in STEM education and workforce training, it is becoming increasingly important to study and understand how learners’ interactions and navigation in virtual environments affect their learning and skill development. In this paper, we quantify and assess the effect of learners’ navigation in an immersive simulated environment on learning outcomes, where navigation is characterized by the total time spent in the simulation and time allocations to different areas within the virtual environment. We implement a set of immersive simulation-based learning (ISBL) modules in an undergraduate computer science course with eighteen students and record their screen as they navigate in the simulation environment to perform the tasks needed to complete the ISBL assignments. We use a video analytics tool to process and analyze the videos and collect statistics related to a set of navigation-related measures for each student. We also use surveys to collect data on students’ demographics, prior knowledge and experience, personality, experiential learning, and self-assessment of learning. We then perform a set of multivariable regression analyses to characterize and explain the relationship between navigation measures and constructs assessed via survey instruments to determine how/if users’ navigation in the simulated environment can be a predictor of their learning outcomes. The results indicate that the total time spent in the simulation and the distribution of time allocations among different areas within the simulated environment are predictors of experiential learning and students’ self-assessment of learning.