Arayüzle etkileşime giren kullanıcının bilişsel yükünün bayes ağına dayalı tahmini

The complexity of human machine interfaces is increasing significantly in parallel with the development of technology and excessive data growth, but human cognitive capacity is limited. Therefore, measuring cognitive load is one of the most preferential and common ways to test the usability of user interfaces. There are many different physiological, behavioral and subjective methods to measure human performance and workload. Moreover, there are cognitive predictive models and many related applications based on these models to predict performance and human workload on computer based tasks. The purpose of this study is to estimate the cognitive load and performance of the person by evaluating multiple methods together based on Bayesian network. For this, we modeled a Bayesian network that both uses a cognitive predictive model, and learns and regulates it with subjective data collected from people. After modelling, we conducted experiments with the interfaces of two different defense projects to collect data. We used the adapted Bedford scale at the end of each task of an interface and the NASA TLX rating scale for the overall rating of the interface after all tasks were completed. We confirmed that the Bayesian network effectively estimated the user's workload and performance. Our findings reveal that this model performs cognitive load analyzes much more efficiently in a short time. This study also demonstrates the differences between tasks and users, providing the opportunity to detect the complexity of subtasks and perform personalized performance and cognitive load analysis for each user.