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Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.
„Bahn-Buddy“ ist eine App, die Kindern bei Bahnfahrten auf unterhaltsame Weise ihre Umgebung näherbringt. Dabei wird mit GPS gearbeitet, um die Inhalte der App auf die jeweilige Bahnstrecke abzustimmen. Verschiedene Minispiele und Infoscreens sollen der Fahrt ein interaktiveres Gefühl verleihen und somit die gefühlte Fahrzeit für die Zielgruppe reduzieren. Den Kindern soll dabei auf eine spielerische Art und Weise Wissen über das Land, aber auch über alternative Verkehrsmittel und den Einfluss des Verkehrs auf die Umwelt vermittelt werden.
Ziel ist es, die generelle Erfahrung des Bahnfahrens positiver zu gestalten. Dies soll zu einer frühen Kundenbindung führen und durch den geringeren CO2-Ausstoß bei Reisen mit der Bahn einen positiven Effekt auf die Umwelt haben.
Adapting augmented reality systems to the users’ needs using gamification and error solving methods
(2021)
Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users’ preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.