The wide variety of services and data available on the internet may make people's lives easier, increasing the access to information and turning services that were complicated into more practical ones. However, the use of computers can be difficult for some people due to issues related to usability, accessibility, or for feeling afraid or anxious while using computers. When this anxiety reaches high levels, they manifest what is known as Computer Anxiety (CA). People with Computer Anxiety (PwCA) may face problems when using computers at home, at work or for study purposes, resulting in multiple forms of barriers even before the actual interaction with a computer. In this context, an eye tracking field study was performed with 39 elderly participants interacting with a website aiming to identify user interface elements impacting negatively task performance and user experience for people with CA. Moreover, an initial exploratory study was performed on the feasibility of creating a classifier for identifying sessions related to people with CA. Results show that certain user interface elements (e.g., carousel and maps) might impact negatively task performance and user experience for PwCA, due to information overload and salient objects calling users' attention. Moreover, classification model using Random Forest reached accuracy of 84.8%. From the presented results, one expects that personalized systems could use classification algorithms to identify sessions from PwCA and then simplify user interfaces based on different levels of CA.