Integrating Bayesian Knowledge Tracing and Human Plausible Reasoning in an Adaptive Augmented Reality System for Spatial Skill Development
Christos Papakostas, Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou
Abstract
The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. This study combines Bayesian Knowledge Tracing (BKT) and Human Plausible Reasoning (HPR) to design an AR system that can adapt to dynamic simulations with quantitative as well as qualitative cognitive methodologies. The system records a broad range of interactions from users, such as objects being rotated, changes in viewing perspective, and time spent on tasks, which are later analyzed through probabilistic updates with respect to skill building along with rule-based reasoning for determining behavioral patterns. Results from an in-depth case study show that the BKT module properly tracks improvement in spatial skills, while the HPR application highlights suboptimal approaches that hide underlying conceptual understanding. The adaptive system used then provides metacognitive hints that adjust by optimizing task difficulty levels, leading to improved student performance compared to standard non-adaptive AR techniques. Results show that using BKT and HPR in an AR environment not only enables accurate task performance but supports greater insight in approach strategies, leading to better and transferable spatial skills.