PREDICTING PROBLEM SOLVING PERFORMANCE IN CAD USERS USING EYE TRACKING AND PERSONALITY TRAITS

Abstract

This study analyses the ability of eye-tracking data and personality traits to predict problem-solving performance in Computer-Aided Design (CAD) users. CAD tasks require a high level of spatial reasoning, attentional control, and strategic planning; thus, it is important to have an understanding of the cognitive and affective constructs driving user performance. This exploratory research focuses on the potential of a predictive model of user performance using real-time visual behaviour analysis and stable personality characteristics.Participants with varied levels of CAD experience were observed completing structured design tasks in a controlled field experiment. While completing the tasks, eye-tracking technology collected behavioural metrics associated with visual attention; fixation duration, saccadic patterns, and gaze heatmaps. Additionally, validated personality inventories were used to gather individual personality trait data in relation to the five personality descriptors; openness, conscientiousness, and neuroticism. Finally, outcomes of task performance were measured in terms of result accuracy, time, and errors.The study yielded meaningful correlations between gaze patterns and overall performance, all of which were moderated by personality profile. For example, conscientious participants with focused gaze trajectories were more efficient complete tasks overall, whereas participants with dispersed visual attention and lower openness performed less efficiently.These results provide exploratory research to indicate that individual psychological profile and real-time behavioural data have potential for a predictive model. Ultimately, the research aims to contribute to the continued development of adaptive CAD systems, that can look to personalize user experience through interfaces thatprovide pivotal information through implications.