Modeling Educational Strategies in Augmented Reality Learning Using Fuzzy Weights

This paper shows the development and application of a fuzzy weight model methodology for the optimization of educational environments in augmented reality. A technology-based learning process in an augmented reality learning environment, notoriously able to improve spatial abilities and increase user engagement with the material learned as well as knowledge retention, has revolutionary potential in education. However, its effectiveness varies greatly. In this, we collaborated with 13 experts in augmented reality and education to deeply evaluate the following six key in-depth strategies: interactive feedback, gamified leaderboards, challenges, personalized learning path, AR-based simulations, and badges. Thus, strategies were rated upon the impact on the learners' outcome, and the ratings were agreed upon as a fuzzy set, because it is a subjective matter of evaluation. Normalization was done to get a balanced measurement that gave weights on which strategies are to be given higher priority. Such a model has been developed and implemented in a case study of AR-based spatial ability training application, focusing on AR-based simulations supplemented with interactive feedback. Pre- and post-tests among 30 respondents have shown significant improvements in the experimental group using the AR application over the control group using traditional means of spatial ability training. The results strongly support the potential of the model for later implementation in AR educational applications that focus on individual specifics and effective learning.