As the amount of information captured about users increased over the last decade, interest in personalized user interfaces has surged in the HCI and IS communities. Personalization is an effective means for accommodating for differences between individuals. The fundamental idea behind personalization rests on the notion that if a system can gather useful information about the user, generate a relevant user model and apply it appropriately, it would be possible to adapt the behavior of a system and its interface to the user at the individual level. Personal-ization of a user interface features can enhance usability. With recent technological advances, personalization can be achieved automatically and unobtrusively. A user interface can deploy a NeuroIS technology such as eye-tracking that learns from the user's visual behavior to provide users an experience most unique to them. The advantage of eye-tracking technology is that subjects cannot consciously manipulate their responses since they are not readily subject to manipulation. The objective of this dissertation is to develop a theoretical framework for user personalization during reading comprehension tasks based on two machine learning (ML) models. The proposed ML-based profiling process consists of user's age characterization and user's cognitive load detection, while the user reads text. To this end, detection of cognitive load through eye-movement features was investigated during different cognitive tasks (see Chapters 3, 4 and 6) with different task conditions. Furthermore, in separate studies (see Chapters 5 and 6) the relationship between user's eye-movements and their age population (e.g., younger and older generations) were carried out during a reading comprehension task. A Tobii X300 eye tracking device was used to record the eye movement data for all studies. Eye-movement data was acquired via Tobii eye tracking software, and then preprocessed and analyzed in R for the aforementioned studies. Machine learning techniques were used to build predictive models. The aggregated results of the studies indicate that machine learning accompanied with a NeuroIS tool like eye-tracking, can be used to model user characteristics like age and user mental states like cognitive load, automatically and implicitly with accuracy above chance (range of 70-92%). The results of this dissertation can be used in a more general framework to adaptively modify content to better serve the users mental and age needs. Text simplification and modification techniques might be developed to be used in various scenarios.
Worcester Polytechnic Institute
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Shojaeizadeh, M. (2018). Automatic Detection of Cognitive Load and User's Age Using a Machine Learning Eye Tracking System. Retrieved from https://digitalcommons.wpi.edu/etd-dissertations/476
Cognitive Load, Mental Effort, Pupillary Response, Eye Tracking, Machine Learning, User Profiling
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