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This study aimed to compare the effects of immersive virtual reality (IVR) videos and 2D educational videos on cognitive (i.e. conceptual knowledge) and non-cognitive (i.e. self-efficacy) learning outcomes. Fifty-three students from an all-girls middle school learned about humans’ impact on the ocean through either IVR videos, using a virtual reality (VR) headset, or through 2D videos, using a computer monitor. Results replicate previous findings suggesting that conceptual knowledge gains between IVR and desktop learning experiences is not significant. Also, results show that participants who watched IVR videos reported higher self-efficacy scores and expressed higher feelings of presence than participants who watched the same videos using a computer monitor. Finally, further analyses revealed that the feeling of presence mediated both cognitive and non-cognitive learning outcomes.
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Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection
Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human–computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.
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- Award ID(s):
- 2305020
- PAR ID:
- 10544699
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Applied Spectroscopy
- Volume:
- 79
- Issue:
- 6
- ISSN:
- 0003-7028
- Format(s):
- Medium: X Size: p. 942-954
- Size(s):
- p. 942-954
- Sponsoring Org:
- National Science Foundation
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