Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights.

Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students' spatial skills. Prior work has investigated the integration of AR technology in engineering education, and more specifically, in spatial ability training. However, the modeling of user knowledge in order to personalize the training has been neither sufficiently explored nor exploited in this task. There is a lot of space for research in this area. In this work, we introduce a novel personalization of the learning path within an AR spatial ability training application. The aim of the research is the integration of Augmented Reality, specifically in engineering evaluation and fuzzy logic technology. During one academic semester, three engineering undergraduate courses related to the domain of spatial skills were supported by a developed adaptive training system named PARSAT. Using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of the Structure of the Observed Learning Outcomes for the design of the learning material, PARSAT offers adaptive learning activities for the students' cognitive skills. Students' data were gathered at the end of the academic semester, and a thorough analysis was delivered. The findings demonstrated that the proposed training method outperformed the traditional method that lacked adaptability, in terms of domain expertise and learning theories, considerably enhancing student learning outcomes.

Measuring User Experience, Usability and Interactivity of a Personalized Mobile Augmented Reality Training System.

Innovative technology has been an important part of firefighting, as it advances firefighters' safety and effectiveness. Prior research has examined the implementation of training systems using augmented reality (AR) in other domains, such as welding, aviation, army, and mathematics, offering significant pedagogical affordances. Nevertheless, firefighting training systems using AR are still an under-researched area. The increasing penetration of AR for training is the driving force behind this study, and the scope is to analyze the main aspects affecting the acceptance of AR by firefighters. The current research uses a technology acceptance model, extended by the external constructs of perceived interactivity and personalization, to consider both the system and individual level. The proposed model was evaluated by a sample of 200 users, and the results show that both the external variables of perceived interactivity and perceived personalization are prerequisite factors in extending the TAM model. The findings reveal that the usability is the strongest predictor of firefighters' behavioral intentions to use the AR system, followed by the ease of use with smaller, yet meaningful, direct and indirect effects on firefighters' intentions. The identified acceptance factors help AR developers enhance the firefighters' experience in training operations.