Abstract
Fundamental concepts of thermodynamics rely on abstract physical quantities such as energy, heat and entropy, which play an important role in the process of interpreting thermal phenomena and statistical mechanics. However, these quantities are not covered by human visual perception, and since heat sensation is purely qualitative and easy to deceive, an intuitive understanding often is lacking. Today immersive technologies like head-mounted displays of the newest generation, especially HoloLens, allow for high-quality augmented reality learning experiences, which can overcome this gap in human perception by presenting different representations of otherwise invisible quantities directly in the field of view of the user on the experimental apparatus, which simultaneously avoids a split-attention effect. In a mixed reality (MR) scenario as presented in this paper-which we call a holo.lab-human perception can be extended to the thermal regime by presenting false-color representations of the temperature of objects as a virtual augmentation directly on the real object itself in real-time. Direct feedback to experimental actions of the users in the form of different representations allows for immediate comparison to theoretical principles and predictions and therefore is supposed to intensify the theory-experiment interactions and to increase students' conceptual understanding. We tested this technology for an experiment on thermal conduction of metals in the framework of undergraduate laboratories. A pilot study with treatment and control groups (N = 59) showed a small positive effect of MR on students' performance measured with a standardized concept test for thermodynamics, pointing to an improvement of the understanding of the underlying physical concepts. These findings indicate that complex experiments could benefit even more from augmentation. This motivates us to enrich further experiments with MR.
Item Type: | Journal article |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science |
Subjects: | 000 Computer science, information and general works > 004 Data processing computer science |
ISSN: | 0143-0807 |
Language: | English |
Item ID: | 66419 |
Date Deposited: | 19. Jul 2019, 12:19 |
Last Modified: | 13. Aug 2024, 12:56 |