Abstract
Affine Transformations (ATs) are a complex and abstract learning content. Encoding the AT knowledge in Game Mechanics (GMs) achieves a repetitive knowledge application and audiovisual demonstration. Playing a serious game providing these GMs leads to motivating and effective knowledge learning. Using immersive Virtual Reality (VR) has the potential to even further increase the serious game’s learning outcome and learning quality. This paper compares the effectiveness and efficiency of desktop-3D and VR in respect to the achieved learning outcome. Also, the present study analyzes the effectiveness of an enhanced audiovisual knowledge encoding and the provision of a debriefing system. The results validate the effectiveness of the knowledge encoding in GMs to achieve knowledge learning. The study also indicates that VR is beneficial for the overall learning quality and that an enhanced audiovisual encoding has only a limited effect on the learning outcome.
Highlights
Affine Transformations (ATs) are part of linear algebra, used for kinematic control [1], computer graphics [2], and development of Virtual Reality (VR) applications
A lack of statistical significance does not imply an equivalence, the results indicate that Gamified Training Environment for Affine Transformations (GEtiT) achieves a similar AT knowledge learning outcome to traditional learning methods, i.e., by using paper-based assignments
No significant difference was found in the learning quality subcategory between the tested learning methods, the results indicate a clear trend that GEtiT and GEtiT VR achieve a higher learning quality
Summary
Affine Transformations (ATs) are part of linear algebra, used for kinematic control [1], computer graphics [2], and development of Virtual Reality (VR) applications. The Gamified Training Environment for Affine Transformations (GEtiT) was developed to address this problem. It intuitively requires the application of ATs and audiovisually demonstrates the underlying theoretical principles [3]. The Gamified Knowledge Encoding utilizes Game Mechanics (GMs) to directly encode a knowledge’s underlying principles as their internal game rules. This achieves a learning content’s repetitive application and audiovisual demonstration during the gameplay. GEtiT embeds the gameplay in complex problems, i.e., an escape scenario, to cause an intrinsic motivation in the learner to tackle the learning assignments
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